Machine Translation: 
its History, Current Status, 
and Future Prospects 
Jonathan Slocum 
Abstract 
Elements ot the history, state of the art, and 
probable future of Machine Translation (MT) are 
discussed. The treatment is largely tutorial, 
based on the assumption that this audience is, for 
the most part, ignorant of matters pertaining to 
translation in general, and MT in particular. The 
paper covers some of the major MT R&D groups, the 
general techniques they employ(ed), and the roles 
they play(ed) in the development of the field. The 
conclusions concern the seeming permanence of the 
translation problem, and potential re-integration 
of MT with mainstream Computational Linguistics. 
Introduction 
Siemens Communications Systems, Inc. 
Linguistics Research Center 
University of Texas 
Austin, Texas 
We are now into the fourth decade of MT, and there 
is a resurgence of interest throughout the world -- 
plus a growing number of ~ and MAT (Machine-aided 
Translation) systems in use by governments, 
business and industry. Industrial firms are also 
beginning to fund M(A)T R&D projects of their own; 
thus it can no longer be said that only goverement 
funding keeps the field alive (indeed, in the U.S. 
there is no government funding, though the Japanese 
and European governments are heavily subsidizing MT 
R&D). In part this interest is due to more 
realistic expectations of what is possible in MT, 
and realization that MT can be very useful though 
imperfect; but it is also true that the 
capabilities of the newer MT systems lie well 
beyond what was possible just one decade ago. 
~chine Translation (MT) of natural human languages 
is not a subject about which most scholars feel 
neutral. Thzs field has had a long, colorful 
career, and boasts no shortage of vociferous 
detractors and proponents alike. During its first 
decade in the 1950"s, interest and support was 
fueled by visions of high-speed high-quality 
translation of arbitrary texts (especially those of 
interest to the military and intelligence 
communities, who funded MT projects quite heavily). 
During its second decade in the 1960"s, 
disillusionment crept in as the number and 
difficulty of the linguistic problems became 
increasingly obvious, and as it was realized that 
the translation problem was not nearly so amenable 
to automated solution as had been thought. The 
climax came with the delivery of the National 
Academy of Sciences ALPAC report in 1966, 
condemning the field and, indirectly, its workers 
allke. The ALPAC report was criticized as narrow, 
biased, and short-sighted, but its recommendations 
were adopted (with the important exception of 
increased expenditures for long-term research in 
computational linguistics), and as a result MT 
projects were cancelled in the U.S. and elsewhere 
around the world. By 1973, the early part of the 
third decade of MT, only three government-funded 
projects were left in the U.S., and by late 1975 
there were none. Paradoxically, MT systems were 
still being used by various government agencies 
here and abroad, because there was simply no 
alternative means of gathering information from 
foreign \[Russian\] sources so quickly; in addition, 
private companies were developing and selling MT 
sysEoms based on the mid-60"s technology so roundly 
castigated by ALPAC. Nevertheless the general 
disrepute of MT resulted in a remarkably quiet 
third decade. 
In light of these events, it is worth reconsidering 
the potential of, and prospects for, Machine 
Translation. After opening with an explanation of 
how \[human\] translation is done where it is taken 
seriously, we will present a brief introduction to 
MT technology and a short historical perspective 
before considering the present status and state of 
the art, and then moving on to a discussion of the 
future prospects. For reasons of space and 
perspicuity, we shall concentrate on MT efforts "in 
the U.S. and western Europe, though some other MT 
projects and less-sophisticated approaches will 
receive attention. 
The Human Translation Context 
When evaluating the feasibility or desirability of 
Machine Translation, one should consider the 
endeavor in light of the facts of human translation 
for like purposes. In the U.S., it is common to 
conceive of translation as simply that which a 
human translator does. It is generally believed 
that a college degree \[or the equivalent\] in a 
foreign language qualifies one to be a translator 
for just about any material whatsoever. Native 
speakers of foreign languages are considered to be 
that much more qualified. Thus, translation is not 
particularly respected as a profession in the U.S., 
and the pay is poor. 
In Canada, in Europe, and generally around the 
world, this myopic attitude is not held. Where 
translation is a fact of life rather than an 
oddity, it is realized that any translator's 
competence is sharply restricted to a few domains 
(this is especially true of technical areas), and 
that native fluency in a foreign language does not 
bestow on one the ability to serve as a translator. 
546 
Thus, there are college-level and post-graduate 
schools that teach the theory (translatology) as 
well as the practice of translation; thus, a 
technical translator is trained in the few areas in 
which he will be doing translation. 
Of special relevance to MT is the fact that 
essentially all translations for dissemination 
(export) are revised by more highly qualified 
translators who necessarily refer back to the 
original text when post-editing the translation. 
(Thls is not "pre-publication stylistic editing".) 
Unrevised translations are always regarded as 
inferior in quality, or at least suspect, and for 
many if not most purposes they are simply not 
acceptable. In the multi-national firm Siemens, 
even internal communications which are translated 
are post-edited. Such news generally comes as a 
surprise, if not a shock, to most people in the US. 
It is easy to see, therefore, that the 
"fully-automatic high-quality machine translation" 
standard, imagined by most U.S. scholars to 
constitute minimum acceptability, must be radically 
redefined. Indeed, the most famous MT critic of 
all eventually recanted his strong opposition to 
MT, admitting that these terms could only be 
defined by the users, according to their own 
standards, for each situation \[Bar-Hillel, 71\]. So 
an FIT system does not have to print and bind the 
result of its translation in order to qualify as 
"fully automatic." '~igh quality" does not at all 
rule out post-editing, since the proscription of 
human revision would "prove" the infeasibility of 
high-quality Human Translation. Academic debates 
about what constitutes "high-quality" and "fully- 
automatic" are considered irrelevant by the users 
of Machine Translation (MT) and Machine-aided 
Translation (MAT) systems; what matters to them are 
two things: whether the systems can produce output 
of sufficient quality for the intended use (e.g., 
revision), and whether the operation as a whole is 
cost-effective or, rarely, justifiable on other 
grounds, like speed. 
Machine Translation Technology 
In order to appreciate the differences among 
translation systems (and their applications), it is 
necessary to understand, first, the broad 
categories into which they can be classified; 
second, the different purposes for which 
translations (however produced) are used; third, 
the intended applications of these systems; and 
fourth, something about the linguistic techniques 
which MT systems employ in attacking the 
translation problem. 
Categories of Systems 
There are three broad categories of "computerized 
translation tools" (the differences hinging on how 
ambitious the system is intended to be): Machine 
Translation (MT), Machine-aided Translation (MAT), 
and Terminology Databanks. 
MT systems are intended to perform translation 
without human intervention. This does not rule out 
pre-processing (assuming this is not for the 
purpose of marking phrase boundaries and resolving 
part-of-speech and/or other ambiguities, etc.), nor 
post-editing (since this is normally done for human 
translations anyway). However, an NT system is 
solely responsible for the complete translation 
process from input of the source text to output of 
the target text without human assistance, using 
special programs, comprehensive dictionaries, and 
collections of linguistic rules (to the extent they 
exist, varying with the NT system). NT occupies 
the top range of positions on the scale of computer 
translation sophistication. 
MAT systems fall into two subgroups: human-assisted 
machine translation (RAMT) and machine-assisted 
human translation (NAHT). These occupy 
successively lower ranges on the scale of computer 
translation sophistication. Ih~HT refers to a 
system wherein the computer is responsible for 
producing the translation per se, but may interact 
with a human monitor at many stages along the way 
-- for example, asking the human to disambiguate a 
word's part of speech or meaning, or to indicate 
where to attach a phrase, or to choose a 
translation for a word or phrase from among several 
candidates discovered in the system's dictionary. 
¥~kHT refers to a system wherein the human is 
responsible for producing the translation per se 
(on-line), but may interact with the system in 
certain prescribed situations -- for example, 
requesting assistance in searching through a local 
dictionary/thesaurus, accessing a remote 
terminology databank, retrieving examples of the 
use of a word or phrase, or performing word 
processing functions like formatting. The 
existence of a pre-processing stage is unlikely in 
a NA(H)T system (the system does not need help, 
instead, it is making help available), but 
post-editing is frequently appropriate. 
Terminology Databanks (TD) are the least 
sophisticated systems because access frequently is 
not made during a translation task (the translator 
may not be working on-line), but usually is 
performed prior to human translation. Indeed the 
databank may not be accessible (to the translator) 
on-line at all, but may be limited to the 
production of printed subject-area glossaries. A 
TD offers access to technical terminology, but 
usually not to common words (the user already knows 
these). The chief advantage of a TD is not the 
fact that it is automated (even with on-line 
access, words can be found just as quickly in a 
printed dictionary), but that it is up-to-date: 
technical terminology is constantly changing and 
published dictionaries are essentially obsolete by 
the time they are available. It is also possible 
for a TD to contain more entries because it can 
draw on a larger group of active contributors: its 
user8. 
The Purposes of Translation 
The most immediate division of translation purposes 
involves information acquisition vs. 
dissemination. The classic example of the former 
purpose is intelligence-gathering: with masses of 
data to sift through, there is no time, money, or 
incentive to carefully translate every document by 
547 
normal (i.e., human) means. Scientists more 
generally are faced with this dilemma: there is 
already more to read than can be read in the time 
available, and having to labor through texts 
written in foreign languages -- when the 
probability is low that any given text is of real 
interest -- is not worth the effort. In the past, 
the lingua franca of science has been English; this 
is becoming less and less true for a variety of 
reasons, including the rise of nationalism and the 
spread of technology around the world. As a 
result, scientists who rely on English are having 
greater difficulty keeping up with work in their 
fields. If a very rapid and inexpensive means of 
translation were available, then -- for texts 
within the reader's areas of expertise -- even a 
low-quality translation might be sufficient for 
information acquisition. At worst, the reader 
could determine whether a more careful (and more 
expensive) translation effort might be justified. 
More likely, he could understand the content of the 
text well enough that a more careful translation 
would not be necessary. 
The classic example of the latter purpose of 
translation is technology export: an industry in 
one country that desires to sell its products in 
another country must usually provide documentation 
in the purchaser's chosen language. In the past, 
U.S. companies have escaped this responsibility by 
requiring that the purchasers learn English; other 
exporters (German, for example) have never had this 
luxury. In the future, with the increase of 
nationalism, it is less likely that English 
documentation will be acceptable. Translation is 
becoming increasingly common as more companies look 
to foreign markets. More to the point, texts for 
information dissemination (export) must be 
translated with a great deal of care: the 
translation must be "right" as well as clear. 
Qualified human technical translators are hard to 
find, expensive, and slow (translating somewhere 
around 4-6 pages/day, on the average). The 
information dissemination application is mast 
responsible for the renewed interest in MT. 
Intended Applications of M(A)T 
Although literary translation is a case of 
information dissemination, there is little or no 
demand for literary translation by machine: 
relative to technical translation, there is no 
shortage of human translators capable of fulfilling 
this need, and in any case computers do not fare 
well at literary translation. By contrast, the 
demand for technical translation is staggering in 
sheer volume; moreover, the acquisition, 
maintenance, and consistent use of valid technical 
terminology is an enormous problem. Worse, in many 
technical fields there is a distinct shortage of 
qualified human translators, and it is obvious that 
the problem will never be alleviated by measures 
such as greater incentives for translators, however 
laudable that may be. The only hope for a solution 
to the technical translation problem lies with 
increased human productivity through computer 
technology: full-scale MT, less ambitious MAT, 
on-line terminology databanks, and word-processing 
all have their place. A serendipitous situation 
involves style: in literary translation, emphasis 
is placed on style, perhaps at the expense of 
absolute fidelity to content (especially for 
poetry). In technical translation, emphasis is 
properly placed on fidelity, even at the expense of 
style. M(A)T systems lack style, but excel at 
terminology: they are best suited for technical 
translation. 
Linguistic Techniques 
There are several perspectives from which one can 
view MT techniques. We will use the following: 
direct vs. indirect; interlingua vs. transfer; 
and local vs. global scope. (Not all eight 
combinations are realized in practice.) We shall 
characterize MT systems from these perspectives, in 
our discussions. In the past, "the use of 
semantics" was always used to distinguish MT 
systems; those which used semantics were labelled 
"good', and those which did not were labelled 
"bad'. Now all MT systems \[are claimed to\] make 
use of semantics, for obvious reasons, so this is 
no longer a distinguishing characteristic. 
'~irect translation" is characteristic of a system 
(e.g., CAT) designed from the start to translate 
out of one specific language and into another. 
Direct systems are limited to the minimom work 
necessary to effect that translation; for example, 
disambiguation is performed only to the extent 
necessary for translation into that one target 
language, irrespective of what might be required 
for another language. "Indirect translation," on 
the other hand, is characteristic of a system 
(e.g., EUROTRA) wherein the analysis of the source 
language and the synthesis of the target language 
are totally independent processes; for example, 
disambiguntion is performed to the extent necessary 
to determine the "meaning" (however represented) of 
the source language input, irrespective of which 
target language(s) that input might be translated 
into. 
The "interlingua" approach is characteristic of a 
system (e.g., CETA) in which the representation of 
the "meaning" of the source language input is 
\[intended to be\] independent of any language, and 
this same representation is used to synthesize the 
target language output. The "linguistic 
universals" searched for and debated about by 
linguists and philosophers is the notion that 
underlies an interlingua. Thus, the representation 
of a given "unit of meaning" would be the same, no 
matter what language (or gr"mm-tical structure) 
that unit might be expressed in. The "transfer" 
approach is characteristic of a system (e.g., TAUM) 
in which the underlying representation of the 
"meaning" of a gr---,-tical unit (e.g., sentence) 
differs depending on the language it was derived 
from \[or into which it is to be generated\]; this 
implies the existence of a third translation stage 
which maps one language-specific meaning 
representation into another: this stage is called 
Transfer. Thus, the overall transfer translation 
process is Analysis followed by Transfer and then 
Synthesis. The "transfer" vs. "interlingua" 
difference is not applicable to all systems; in 
particular, "direct" MT systems use neither the 
548 
transfer nor the interlingua approach, since they 
do not attempt to represent "meaning'. 
'~ocal scope" vs. "global scope" is not so much a 
difference of category as degree. '~ocal scope" 
characterizes a system (e.g., SYSTRAN) in which 
words are the essential unit driving analysis, and 
in which that analysis is, in effect, performed by 
separate procedures for each word which try to 
determine -- based on the words to the left and/or 
right -- the part of speech, possible idiomatic 
usage, and "sense" of the word keying the 
procedure. In such systems, for example, 
homographs (words which differ in part of speech 
and/or derivstional history \[thus meaning\], but 
which are written alike) are a major problem, 
because s unified analysis of the sentence per se 
is not attempted. "Global scope" characterizes a 
system (e.g., METAL) in which the meaning of a word 
is determined by its context within a unified 
analysis of the sentence (or, rarely, paragraph). 
In such systems, by contrast, homographs do not 
typically constitute a significant problem because 
the amount of context taken into account is much 
greater than is the case with systems of "local 
scope. " 
Historical Perspective 
There are several comprehensive treatments of MT 
projects \[Bruderer, 77\] and MT history \[Hutchins, 
78\] available in the open literature. To 
illustrate some continuity in the field of MT, 
while remaining within reasonable space limits, our 
brief historical overview will be restricted to 
defunct systems/projects which gave rise to 
follow-on systems/projects of current interest. 
THese are: Georgetown's CAT, Grenoble's CETA, 
Texas" METAL, Montreal's TAUM, and Brigham Young 
University's ALP system. 
CAT - Georgetown Automatic Translation 
Georgetown University was the site of one of the 
earllest MT projects. Begun in 1952, and supported 
by the U.S. government, Georgetown's CAT system 
became operational in 1964 with its delivery to the 
Atomic Energy Commission at Oak Ridge National 
Laboratory, and to Europe's corresponding research 
facility EURATON in Ispra, Italy. Both systems 
were used for many years to translate Russian 
physics texts into "English." The output quality 
was quits poor, by comparison with human 
translations, but for the intended purpose of 
quickly scanning documents to determine their 
content and interest, the CAT system was 
nevertheless superior to the only alternatives: 
slow and more expensive human translation or, 
worse, no translation at all. GAT was not replaced 
at EURATOM until 1976; at ORNL, it seems to have 
been used until around 1979 \[Jordan et el., 76, 
77\]. 
The GAT strategy was "direct" and "local": simple 
word-for-word replacement, followed by a limited 
amount of transposition of words to result in 
something vaguely resembling English. Very soon, a 
"word" came to be defined as a single word or a 
sequence of words forming an "idiom'. There was no 
true linguistic theory underlying the GAT design; 
and, given the state of the art in computer 
science, there was no underlying computational 
theory either. GAT was developed by being made to 
work for a given text, then being modified to 
account for the next text, and so on. The eventual 
result was a monolithic system of intractable 
complexity: after its delivery to ORNL and EURATOM, 
it underwent no significant modification. The fact 
that it was used for so long is nothing short of 
remarkable -- a lesson in what can be tolerated by 
users who desperately need translation services for 
which there is no viable alternative to even 
low-quality MT. 
The termination of the Georgetown MT project in the 
mid-60"s resulted in the incorporation of LATSEC by 
Peter Tome, one of the GAT workers. LATSEC soon 
developed the SYSTRAN system (based on GAT 
technology), which in 1970 replaced the IBM Mark II 
system at the USAF Foreign Technology Division 
(FTD) at Wright Patterson AYB, and in 1976 replaced 
GAT at EURATOM. SYSTRAN is still being used to 
translate Russian into English for 
information-acquisition purposes. We shall return 
to our discussion of SYSTRAN in the next major 
section. 
CETA - Centre d'~tudes pour la Traduction 
Automatique 
In 1%1 a project was started at Grenoble 
University in France, to translate Russian into 
French. Unlike CAT, Grenoble began the CETA 
project with a clear linguistic theory-- having 
had a number of years in which to witness and learn 
from the events transpiring at Georgetown and 
elsewhere. In particular, it was resolved to 
achieve a dependency-structure analysis of every 
sentence (a "global" approach) rather than rely on 
intra-sentential heuristics to control limited word 
transposition (the "local" approach); with a 
unified analysis in hand, a reasonable synthesis 
effort could be mounted. The theoretical basis of 
CETA was "interlingua" (implying a language- 
independent, "neutral" meaning representation) at 
the gr-mm-tical level, hut "transfer" (implying a 
mapping from one language-specific meaning 
representation to another) at the lexical 
\[dictionary\] level. The state of the art in 
computer science still being primitive, Grenoble 
was essentially forced to adopt IBM assembly 
language as the software basis of CETA \[Rutchins, 
78\]. 
The CETA system was under development for ten 
years; during 1%7-71 it was used to translate 
400,000 words of Russian mathematics and physics 
texts into French. The major findings of this 
period were that the use of an interlingua erases 
all clues about how to express the translation; 
also, that it results in extremely poor or no 
translations of sentences for which complete 
analyses cannot be derived. The CETA workers 
learned that it is critically important in an 
operational system to retain surface clues about 
how to formulate the translation (Indo-European 
languages, for example, have many structural 
similarities, not to mention cognates, that one can 
549 
take advantage of), and to have "fail-soft" 
measures designed into the system. An interlingua 
does not allow this \[easily, if at all\], but the 
transfer approach does. 
A change in hardware (thus software) in 1971 
prompted the abandonment of the CETA system, 
immediately followed by the creation of a new 
project/system called GETA, based entirely on a 
fail-soft transfer design. The software was still, 
however, written in assembly language; this 
continued reliance on assembly language was soon to 
have deleterious effects, for reasons now obvious 
to anyone. We will return to our discussion of 
GETA, below. 
METAL - MEchanical Translation and Analysis of 
Languages 
Having had the same opportunity for hindsight, the 
University of Texas in 1961 used U.S. government 
funding to establish the Linguistics Research 
Center, and with it the METAL project, to 
investigate MT -- not from Russian, but from German 
into English. The LRC adopted Chomsky's 
transformational paradigm, which was quickly 
gaining popularity in linguistics circles, and 
within that framework employed a syntactic 
interl~ngua based on deep structures. It was soon 
discovered that transformational linguistics per se 
was not sufficiently well-developed to support an 
operational system, and certain compromises were 
made. The eventual result, in 1974, was an 
80,000-1ine, 14-overlay FORTRAN program running on 
a dedicated CDC 6600. Indirect translation was 
performed in 14 steps of global analysis, transfer, 
and synthesis -- one for each of the 14 overlays -- 
and required prodigious amounts of CPU time and I/O 
from/to massive data files. U.S. government 
support for MT projects was winding down in any 
case, and the METAL project was shortly terminated. 
Several years later, a small Government grant 
resurrected the project. The FORTRAN program was 
rewritten in LISP to run on a DEC-10; in the 
process, it was pared down to just three major 
stages (analysis, transfer, and synthesis) 
comprising about 4,000 lines of code which could be 
accommodated in three "overlays," and its computer 
resource requirements were reduced by a factor of 
ten. Though U.S. government interest once again 
languished, the Sprachendienst (Language Services) 
department of Siemens b~ in Munich had begun 
supporting the project, and in 1980 Siemens AG 
became the sole sponsor. 
TAUM - Traduction Automatique de l'Universit~ de 
Hontr~al 
In 1962 the University of Montreal established the 
TAUM project with Canadian government funding. 
This was probably the first MT project designed 
strictly around the transfer approach. As the 
software basis of the project, TAUM chose the 
PASCAL programming language on the CDC 6600. After 
an initial period of more-or-less open-ended 
research, the Canadian gover~m~ent began adopting 
specific goals for the TAUM system. A chance 
remark by a bored translator in the Canadian 
Meteorological Center had led to a spin-off 
project: TAUM-METEO. Weather forecasters were 
already required to adhere to a prescribed manual 
of style and vocabulary in their English reports. 
Partly as a result of this, translation into French 
was so monotonous a task that human translator 
turnover in the weather service was extraordinarily 
high -- six months was the average tenure. TAUM 
was commissioned in 1975 to produce an operational 
English-French MT system for weather forecasts. A 
prototype was demonstrated in 1976, and by 1977 
METEO was installed for production translation. We 
will discuss METEO in the next major section. 
The next challenge was not long in coming: by a 
fixed date, TAUM had to be usable for the 
translation of a 90 million word set of aviation 
maintenance manuals from English into French (else 
the translation had to he started by human means, 
since the result was needed quickly). From this 
point on, TAUM concentrated on the aviation manuals 
exclusively. To alleviate problems with their 
purely syntactic analysis (especially considering 
the many multlple-noun compounds present in the 
aviation manuals), the group began in 1977 to 
incorporate partial semantic analysis in the 
TAUM-AVLkTION system. 
After a test in 1979, it became obvious that 
TAUM-AVIATION was not going to be production-ready 
in time for its intended use. The Canadian 
goverement organized a series of tests and 
evaluations to assess the status of the system. 
Among other things, it was discovered that the cost 
of writing each dictionary entry was remarkably 
high (3.75 man-hours, costing $35-40), and that the 
system's runtime translation cost was also high (6 
cents/word) considering the cost of human 
translation (8 cents/word), especially when the 
post-editing costs (10 cents/word for TAUM vs. 4 
cents/word for human translations) were taken ihto 
account \[Gervais, 1980\]; TAUM was not yet 
cost-effective. Several other factors, especially 
the bad Canadian economic situation, combined with 
this to cause the cancellation of the TAUM project 
in 1981. There are recent signs of renewed 
interest in MT in Canada. State-of-the-art surveys 
have been commissioned \[Pierre Isabelle, formerly 
of TAUM, personal communication\], but no successor 
project has yet been established. 
ALP - Automated Language Processing 
In 1971 a project was established at Brigham Young 
University to translate Mormon ecclesiastical texts 
from English into multiple languages -- starting 
with French, German, Portuguese and Spanish. The 
eventual aim was to produce a fully-automatic MT 
system based on Junction Grammar \[Lytle et al., 
75\], but actual work proceeded on Machine-Aided 
Translation (MAT, where the system does not attempt 
to analyze sentences on its own, according to 
pre-programmed linguistic rules, but instead relies 
heavily on interaction with a human to effect the 
analysis \[if one is even attempted\] and complete 
the translation). 
The BYU project never produced an operational 
system, and the Mormon Church, through the 
550 
University, began to dismantle the project. Around 
1977, a group composed primarily of programmers 
left BYU to join Weidner Communications, Inc., and 
proceeded to develop the fully-automatic, direct 
Weidner MT system. Shortly thereafter, most of the 
remaining BYU project members left to form 
Automated Language Processing Systems (ALPS) and 
continue development of the BYU MAT system. Both 
of these systems are actively marketed today, and 
will be discussed in the next section. Some work 
continues at BYU, but at a very much reduced level 
and degree of aspiration (e.g., \[Melby, 82\]). 
Current Production Systems 
In this section we consider the major M(A)T systems 
being used and/or marketed today. Four of these 
originate from the "failures" described above, but 
four systems are essentially the result of 
successful (i.e., continuing) MT R&D projects. The 
full MT systems discussed below are the following: 
SYSTRAN, LOGOS, METEO, Weidner, and SPANAM; we will 
also discuss the MAT systems CULT and ALPS. Most 
of these systems have been installed for several 
customers (METEO, SPANAM, and CULT ere the 
exceptions, with only one obvious "user" each). 
The oldest installation dates from 1970. 
A "standard installation," if it can be said to 
exist, includes provision for pre-processing in 
some cases, translation (with much human 
intervention in the case of MAT systems), and some 
amount of post-editing. To MT system users, 
acceptability is a function of the amount of pre- 
and/or post-editing that must be done (which is 
also the greatest determinant of cost). Van Slype 
\[82\] reports that "acceptability to the human 
translator...appears negotiable when the quality of 
the MT system is such that the correction (i.e., 
post-editing) ratio is lower than 20% (i correction 
every 5 words) and when the human translator can be 
associated with the upgrading of the MT system." 
It is worth noting that editing time has been 
observed to fall with practice: Pigott \[82\] reports 
that "...the more M.T. output a translator 
handles, the more proficient he becomes in making 
the best use of this new tool. In some cases he 
manages to double his output within a few months as 
he begins to recognize typical M.T. errors and 
devise more efficient ways of correcting them." 
It is also important to realize that, though none 
of these systems produces output mistakable for 
human translation \[at least not good human 
translation\], their users have found sufficient 
reason to continue using them. Some users, indeed, 
are repeat customers. In short, FIT & MAT systems 
cannot be argued not to work, for they are in fact 
being bought and used, and they save time and/or 
money for their users. Every user eXpresses a 
desire for improved quality and reduced cost, to be 
sure, but then the same is said about human 
translation. Thus, in the only valid sense of the 
idiom, MT & MAT have already "arrived." Future 
improvements in quality, and reductions in cost -- 
both certain to take place -- will serve to make 
M(A)T systems even more attractive. 
SYSTRAN 
SYSTRAN was one of the first MT systems to be 
marketed; the first installation replaced the IBM 
Mark II Russian-English system at the USAF FTD in 
1970, and is still operational, Eased on the CAT 
technology (SYSTRAN uses the same linguistic 
strategies, to the extent they can be argued to 
exist), SYSTRAN's software basis has been much 
improved by the introduction of modularity 
(separating the analysis and synthesis stages), by 
a recent shift away from simple "direct" 
translation (from the Source Language straight into 
the Target Language) toward the inclusion of 
something resembling an intermediate "transfer" 
stage, and by the allowance of manually-selected 
topical glossaries (essentially, dictionaries 
specific to \[the subject area of\] the text). The 
system is still ad hoc -- particularly in the 
assignment of semantic features \[Pigott, 79\]. The 
USAF FTD dictionaries number over a million 
entries; Eostad \[82\] reports that dictionary 
updating must be severely constrained, lest a 
change to one entry disrupt the activities of many 
others. (A study by Wilks \[78\] reported an 
improvement/degradation ratio \[after dictionary 
updates\] of 7:3, but Bostad implies a much more 
stable situation after the introduction of 
stringent \[and expensive\] quality-control 
measures.) NASA selected SYSTRAN in 1974 to 
translate materials relating to the Apollo-Soyuz 
collaboration, and EURATOM replaced GAT with 
SYSTRAN in 1976. Also by 1976, FTD was augmenting 
SYSTRA~ with word-processing equipment to increase 
productivity (e.g., to eliminate the use of 
punch-cards). 
In 1976 the Commission of the European Communities 
purchased an English-French version of SYSTRAN for 
evaluation and potential use. Unlike the FTD, 
NASA, and EURATOM installations, where the goal was 
information acquisition, the intended use by CEC 
was for information dissemination -- meaning that 
the output was to be carefully edited before human 
consumption. Van Slype \[82\] reports that "the 
English-French standard vocabulary delivered by 
Prof. Toma to the Commission was found to be 
almost entirely useless for the Commission 
enviror--ent. '' Early evaluations were negative 
(e.g., Van Slype \[79\]), but the existing and 
projected overload on CEC human translators was 
such that investigation continued in the hope that 
dictionary additions would improve the system to 
the point of usability. Additional versions of 
SYSTRAN were purchased (French-English in 1978, and 
Engllsh-Italian in 1979). The dream of acceptable 
quality for post-editing purposes was eventually 
realized: Pigott \[82\] reports that "...the 
enthusiasm demonstrated by \[a few translators\] 
seems to mark something of a turning point in 
\[machine translation\]." Currently, about 20 CEC 
translators in Luxambourg are using SYSTRAN on a 
Siamens 7740 computer for routine translation; one 
factor accounting for success is that the English 
and French dictionaries now consist of well over 
i00,000 entries in the very few technical areas for 
which SYSTRAN is being employed. 
551 
Also in 1976, General Motors of Canada acquired 
SYSTRAN for translation of various manuals (for 
vehicle service, diesel locomotives, and highway 
transit coaches) from English into French on an IBM 
mainframe. GM's English-French dictionary had been 
expanded to over 130,000 terms by 1981 \[Sereda, 
82\]. Subsequently, GM purchased an English-Spanish 
version of SYSTRAN, and is now working to build the 
necessary \[very large\] dictionary. Sereda \[82\] 
reports a speed-up of 3-4 times in the productivity 
of his human translators (from about 1000 words per 
day); he also reveals that developing SYSTRAN 
dictionary entries costs the company approximately 
$4 per term (word- or idiom-pair). 
While other SYSTRAN users have applied the system 
to unrestricted texts (in selected subject areas), 
Xerox has developed a restricted input language 
('Multinational Customized English') after 
consultation with LATSEC. That is, Xerox requires 
its English technical writers to adhere to a 
specialized vocabulary and a strict manual of 
style. SYSTRAN is then employed to translate the 
resulting documents into French, Italian, and 
Spanish; Xerox hopes to add German and Portuguese. 
Ruffino \[82\] reports "a five-to-one gain in 
translation time for most texts" with the range of 
gains being 2-10 times. This approach is not 
necessarily feasible for all organizations, but 
Xerox is willing to employ it and claims it also 
enhances source-text clarity. 
Currently, SYSTRAN is being used in the CEC for the 
routine translation, followed by human 
post-editing, of around 1,000 pages of text per 
month in the couples English-French, 
French-English, and English-ltalian \[Wheeler, 83\]. 
Given this relative success in the CEC envirom-ent, 
the Commission has recently ordered an 
English-German version as well as a French-German 
version. Judging by past experience, it will be 
quite some time before these are ready for 
production use, but when ready they will probably 
save the CEC translation bureau valuable time, if 
not real money as well. 
LOGOS 
Development of the LOGOS system was begun in 1964. 
The first installation, in 1971, was used by the 
U.S. Air Force to translate English maintenance 
manuals for military equipment into Vietnamese. 
Due to the termination of U.S. involvement in that 
war, and perhaps partly to a poor evaluation of 
LOGOS" cost-effectiveness \[Sinaiko and Xlare, 73\], 
its use was ended after two years. As with 
SYSTRAN, the linguistic foundations of LOGOS are 
weak and inexplicit (they appear to involve 
dependency structures); and the analysis and 
synthesis rules, though separate, seem to be 
designed for particular source and target 
languages, limiting their extensibility. 
LOCOS continued to attract customers. In 1978, 
Siemens AG began funding the development of a LOGOS 
German-English system for telecommunications 
manuals. After three years LOCOS delivered a 
"production" system, but it was not found suitable 
for use (due in part to poor quality of the 
translations, and in part to the economic situation 
within Siemens which had resulted in a much-reduced 
demand for translation, hence no immediate need for 
an MT system). Eventually LOGOS forged an 
agreement with the Wang computer company which 
allowed LOGOS to implement the German-English 
system (formerly restricted to large IBM 
mainframes) on Wang office computers. This system 
is being marketed today, and has recently been 
purchased by the Commission of the European 
Communities. Development of other language pairs 
has been mentioned from time to time. 
METEO 
TAUM-METEO is the world's only example of a truly 
fully-automatic MT system. Developed as a spin-off 
of the TAUM technology, as discussed earlier, it 
was fully integrated into the Canadian 
Meteorological Center's (CMC's) nation-wide weather 
communications network by 1977. METEO scans the 
network traffic for English weather reports, 
translates them "directly" into French, and sends 
the translations back out over the communications 
network automatically. Rather than relying on 
post-editors to discover and correct errors, METEO 
detects its own errors and passes the offending 
input to human editors; output deemed "correct" by 
METEO is dispatched without human intervention, or 
even overview. 
TAUM-METEO was probably also the first MT system 
where translators were involved in all phases of 
the design/development/refinement; indeed, a CMC 
translator instigated the entire project. Since 
the restrictions on input to METEO were already in 
place before the project started (i.e., METEO 
imposed no new restrictions on weather 
forecasters), METEO cannot quite be classed with 
the TITUS and Xerox SYSTRAN systems which rely "on 
restrictions geared to the characteristics of those 
MT systems. But METEO is not extensible. 
One of the more remarkable side-effects of the 
METEO installation is that the translator turn-over 
rate within the CMC went from 6 ~nths, prior to 
METEO, to several years, once the CMC translators 
began to trust METEO's operational decisions and 
not review its output \[Brian Harris, personal 
communication\]. METEO's input constitutes over 
11,000 words/day, or 3.5 million words/year. Of 
this, it correctly translates 80%, shuttling the 
other ('bore interesting") 20% to the human CMC 
translators; almost all of these "analysis 
failures" are attributable to violations of the CMC 
language restrictions, though some are due to the 
inability of the system to handle certain 
constructions. METEO's computational requirements 
total about 15 CPU minutes per day on a CDC 7600 
\[Thouin, 82\]. By 1981, it appeared that the 
built-in limitations of METEO's theoretical basis 
had been reached, and further improvement was not 
possible. 
Weidner Communications Systems, Inc. 
Weidner was established in 1977 by Bruce Weidner, 
who hired a group of FIT workers (predominantly 
programmers) from the fading BYU project. Weidner 
552 
delivered a production English-French system to 
Mitel in Canada in 1980, and a beta-test 
English-Spanish system to the Siemens Corporation 
(USA) in the same year. In 1981 Mite1 took 
delivery on Weidner's English-Spanish and 
English-German systems, and Bravice (a translation 
service bureau in Japan) purchased the Weidner 
English-Spanish and Spanish-English systems. To 
date, there are about 22 installations of the 
Weidner MT system around the world. The Weidner 
system, though "fully automatic" during 
translation, is marketed as a "machine aid" to 
translation (perhaps to avoid the stigma usually 
attached to MT). It is highly interactive for 
other purposes (the lexical pre-analysis of texts, 
the construction of dictionaries, etc.), and 
integrates word-processing software with external 
devices (e.g., the Xerox 9700 laser printer at 
Mitel) for enhanced overall document production. 
Thus, the Weidner system accepts a formatted source 
document (actually, one containing 
formatting/typesetting codes) and produces a 
formatted translation. This is an important 
feature to users, since almost everyone is 
interested in producing formatted translations from 
formatted source texts. 
Given the way this system is tightly integrated 
with moaern word-processing technology, it is 
difficult to assess the degree to which the 
translation component itself enhances translator 
productlvity, vs. the degree to which simple 
automation of formerly manual (or poorly automated) 
processes accounts for the productivity gains. The 
"direct" translation component itself is not 
particularly sophisticated. For example analysis 
is "local," being restricted to the noun phrase or 
verb phrase level -- so that context available only 
at higher levels can never be taken into account. 
Translation is performed in four independent 
stages: idiom search, homograph disambiguation, 
structural analysis, and transfer. These stages do 
not interact with each other, which creates more 
problems; for example, an apparent idiom in a text 
is always treated idiomatically -- never literally, 
no matter what its context (since no other 
contextual information is available until later). 
Hundt \[82\] comments that "idioms are an extremely 
important part of the translation procedure." It 
is particularly interesting that he continues: 
"...machine assisted translation is for the most 
part word replacement..." Then, "It is not 
worthwhile discussing the various problems of the 
\[Weidner\] system in great depth because in the 
first place they are much too numerous..." Yet 
even though the Weidner translations are of low 
quality, users nevertheless report economic 
satisfaction with the results. Hundt continues 
"...the Weidner system indeed works as an aid..." 
and, "800 words an hour as a final figure \[for 
translation throughput\] is not unrealistic." This 
level of performance was not attainable with 
previous \[human\] methods, and some users report the 
use of Weidner to be cost-effective, as well as 
faster, in their enviroements. 
In 1982, Weidner delivered English-German and 
German-English systems to ITT in Great Britain; but 
there were some financial problems (a third of the 
employees were laid off that year) until a 
controlling interest was purchased by a Japanese 
company: Bravice, one of Weidner's customers, owned 
by a group of wealthy Japanese investors. Weidner 
continues to market }iT systems, and is presently 
working to develop Japanese MT systama. A 
prototype Japanese-English system has recently been 
installed at Bravice, and work continues on an 
English-Japanese system. In addition, Weidner has 
implemented its systam on the IBM Personal 
Computer, in order to reduce its former dependence 
on the PDP-II. 
SPANAM 
Following a promising feasiblity study, the Pan 
American Health Organization in Washington, D.C. 
decided in 1975 to undertake work on a machine 
translation system, utilizing many of the same 
techniques developed for GAT; consultants were 
hired from nearby Georgetown University, the home 
of GAT. The official PAHO languages are English, 
French, Portuguese, and Spanish; Spanish-English 
was chosen as the initial language pair, due to the 
belief that "This combination requires fewer 
parsing strategies in order to produce manageable 
output \[and other reasons relating to expending 
effort on software rather than linguistic rules\]" 
\[Vasconcellos, 83\]. Actual work started in 1976, 
and the first prototype was running in 1979, using 
punched card input on an IBM mainframe. With the 
subsequent integration of a word processing system, 
production use could be seriously considered. 
After further upgrading, the system in 1980 was 
offerred as a service to potential users. Later 
that year, in its first major test, SPANAM reduced 
manpower requirements for a certain translation 
effort by 45~, resulting in a monetary savings of 
61Z \[Vasconcellos, 83\]. Since then it has been 
used to translate well over a million words of 
text, averaging about 4,000 words per day per 
post-editor. (Significantly, SPANAM's in-house 
developers seem to be the only revisors of its 
output.) The post-editors have amassed "a bag of 
tricks" for speeding the revision work, and special 
string functions have also been built into the word 
processor for handling SPANAM's English output. 
Sketchy details imply that the linguistic 
technology underlying SPANAM is essentially that of 
GAT; the rules may even still be built into the 
programs. The software technology has been updated 
considerably in that the programs are modular (in 
the newest version). The total lack of 
sophistication by modern Computational Linguistics 
standards is evidenced by the offhand remark that 
"The maximum length of an idiom \[allowed in the 
dictionary\] was increased from five words to 
twenty-five" in 1980 \[Vasconcellos, 83\]. Also, the 
system adopts the "direct" translation strategy, 
and fails to attempt a "global" analysis of the 
sentence, settling for "local" analysis of limited 
phrases. The SPANAM dictionary currently numbers 
55,000 entries. A follow-on project to develop 
ENGSPAN, underway since 1981, has produced some 
test translations. 
553 
CULT - Chinese University Language Translator 
CULT is perhaps the most successful of the 
Machine-aided Translation systems. Development 
began at the Chinese University of Hong Kong around 
1968. CULT translates Chinese mathematics and 
physics journals (published in Beijing) into 
English through a highly-interactive process \[or, 
at least, with a lot of human intervention\]. The 
goal was to eliminate post-editing of the results 
by allowing a large amount of pre-editing of the 
input, and a certain \[unknown\] degree of human 
intervention during translation. Although 
published details \[Loh, 76, 78, 79\] are not 
unambiguous, it is clear that humans intervene by 
marking sentence and phrase boundaries in the 
input, and by indicating word senses where 
necessary, among other things. (What is not clear 
is whether this is strictly a pre-editing task, or 
an interactive task.) CULT runs on the ICL 1904A 
computer. 
Beginning in 197~, the CULT system was applied to 
the task of translating the Acta Mathematica Sinica 
into English; in 1976, this was joined by the Acta 
Physica Sinlca. This production translation 
practice continues to this day. Originally the 
Chinese character transcription problem was solved 
by use of the standard telegraph codes invented a 
century ago, and the input data was punched on 
cards. But in 1978 the system was updated by the 
addition of word-processing equipment for on-line 
data entry and pre/post-editing. 
It is not clear how general the techniques behind 
CULT are -- whether, for example, it could be 
applied to the translation of other texts -- nor 
how cost-effective it is in operation. Other 
factors may justify its continued use. It is also 
unclear whether R&D is continuing, or whether CULT, 
like METEO, is unsuited to design modification 
beyond a certain point already reached. In the 
absence of answers to these questions, and perhaps 
despite them, CULT does appear to be an MAT success 
story: the amount of post-editing said to be 
required is trivial -- limited to the 
re-introduction of certain untranslatable formulas, 
figures, etc., into the translated output. At some 
point, other translator intervention is required, 
but it seems to be limited to the manual inflection 
of verbs and nouns for tense and number, and 
perhaps the introduction of a few function words 
such as English determiners. 
ALPS - Automated Language Processing Systems 
ALPS was incorporated by another group of Brigham 
Young University workers, around 1979; while the 
group forming Weidner was composed mostly of the 
programmers interested in producing a 
fully-automatic MT system, the group forming ALPS 
(reusing the old BYU acronym) was composed mostly 
of linguists interested in producing machine aids 
for human translators (dictionary look-up and 
substitution, etc.) \[Melby and Tenney, personal 
communication\]. Thus the ALPS system is 
interactive in all respects, and does not seriously 
pretend to perform translation at all; rather, ALFS 
provides the translator with a set of software 
tools to automate many of the tasks encountered in 
everyday translation experience. ALPS adopted the 
tools originally developed at BYU -- and hence, the 
language pairs the BYU system had supported: 
English into French, German, Portuguese, and 
Spanish. Since then, other languages (e.g., 
Arabic) have been announced, but their commercial 
status is unclear. 
The ALPS system is intended to work on any of three 
"levels" -- providing capabilities from simple 
dictionary lookup on demand to word-for-word 
(actually, term-for-term) translation and 
substitution into the target text. The central 
tool provided by ALPS is a menu-driven 
word-processing system coupled to the on-line 
dictionary. One of the first ALPS customers seems 
to have been Agnew TechTran -- a commercial 
translation bureau which acquired the ALP$ system 
for in-house use. Recently, another change of 
ownership and consequent shake-up at Weidner 
communication Systems, Inc., has allowed ALPS to 
hire a large group of former Weidner workers, 
leading to speculation that ALPS might itself be 
intending to enter the MT arena. 
Current Research and Development 
In addition to the organizations marketing or using 
existing M(A)T systems, there are several groups 
engaged in on-going R&D in this area. Operational 
(i.e., marketed or used) systems have not yet 
resulted from these efforts, but deliveries are 
foreseen at various times in the future. We will 
discuss the major Japanese MT efforts briefly (as 
if they were unified, in a sense, though for the 
most part they are actually separate), and then the 
major U.S. and European MT systems at greater 
length. 
MT R&D in Japan 
In 1982 Japan electrified the technological world 
by widely publicizing their new Fifth Generation 
project and establishing the Institute for New 
Generation Computer Technology (ICOT) as its base. 
Its goal is to leapfrog Western technology and 
place Japan at the forefront of the digital 
electronics world in the 1990"s. MITI (Japan's 
Ministry of International Trade and Industry) is 
the motivating force behind this project, and 
intends that the goal be achieved through the 
development and application of highly innovative 
techniques in both computer architecture and 
Artificial Intelligence. 
Of the research areas to be addressed by the ICOT 
scientists and engineers, Machine Translation plays 
a prominent role. Among the western Artificial 
Intelligentsia, the inclusion of D~ seems out of 
place: AI researchers have been trying 
(successfully) to ignore all MT work in the two 
decades since the ALPAC debacle, and almost 
universally believe that success is impossible in 
the foreseeable future -- in ignorance of the 
successful, cost-effective applications already in 
place. To the Japanese leadership, however, the 
inclusion of D~ is no accident. Foreign language 
training aside, translation into Japanese is still 
554 
one of the primary means by which Japanese 
researchers acquire information about what their 
Western competitors are doing, and how they are 
doing it. Translation out of Japanese is necessary 
before Japan can export products to its foreign 
markets, because the customers demand that the 
manuals and other documentation not be written only 
in Japanese. The Japanese correctly view 
translation as necessary to their technological 
survival, but have found it extremely difficult to 
accomplish by human means. Accordingly, their 
government has sponsored MT research for several 
decades. There has been no rift between AI and D~ 
researchers in Japan, as there has been in the West 
-- especially in the U.S. MT may even be seen as 
the key to Japan's acquisition of enough Western 
technology to train their scientists and engineers, 
and thus accomplish their Fifth Generation project 
goals. 
Nemura \[82\] nembers the MT R&D groups in Japan at 
more than eighteen. (By contrast, there might be a 
dozen significant MT groups in all of the U.S. and 
Europe, including commercial vendors.) Several of 
the Japanese projects are quite large. (By 
contrast, only one MT project in the western world 
\[EUROTRA\] even appears as large, but most of the 80 
individuals involved work on EUROTRA only a 
fraction of their time.) Most of the Japanese 
projects are engaged in research as much as 
development. (Most Western projects are engaged in 
development.) Japanese progress in MT has not come 
fast: until a few years ago, their hardware 
technology was inferior; so was their software 
competence, but this situation has been changing 
rapidly. Another obstacle has been the great 
differences between Japanese and Western languages 
-~ especially English, which is of greatest 
interest to them -- and the relative paucity of 
knowledge about these differences. The Japanese 
are working to eliminate this ignorance: progress 
has been made, and production-quality systems 
already exist for some applications. None of the 
Japanese MT systems are "direct," and all engage in 
"global" analysis; most are based on a transfer 
approach, but a few groups are pursuing the 
interlingua approach. 
MT research has been pursued at Kyoto University 
since 1968. There are now two MT projects at Kyoto 
(one for near-term application, one for long-term 
research). The former has developed a practical 
system for translating English titles of scientific 
and technical papers into Japanese \[Nagao, 80, 82\], 
and is working on other applications of 
English-Japanese \[Tsujii, 82\] as well as 
Japanese-English \[Nagao, 81\]. The other group at 
Kyoto is working on an English-Japanese translation 
system based on formal semantics (Cresswell's 
simplified version of Montague Grammar \[Nishida et 
al., 82, 83j). Kyushu University has been the home 
of HT research since 1955, with projects by Tamachi 
and Shudo \[74\]. The University of Osaka Prefecture 
and Fukuoka University also host MT projects. 
However, most Japanese D~ research (like other 
research) is performed in the industrial 
laboratories. Fujitsu \[Sawai et al., 82\], Hitachi, 
Toshiba \[Amano, 82\], and NEC \[Muraki & Ichiyema, 
82\], among others, support large projects generally 
concentrating on the translation of computer 
manuals. Nippon Telegraph and Telephone is working 
on a system to translate scientific and technical 
articles from Japanese into English and vice versa 
\[Nemura et al., 82\], and is looking into the future 
as far as simultaneous machine translation of 
telephone conversations \[Nemura, personal 
communication\]. 
The Japanese industrialists are not confining their 
attention to work at home. Several AI/MT groups in 
the U.S. (e.g., SRI, U. Texas) have been 
approached by Japanese companies desiring to fund 
MT R&D projects. More than that, some U.S. MT 
vendors (SYSTRAN and Weidner, at least) have 
recently sold partial interests to Japanese 
investors. Various Japanese corporations (e.g., 
NTT and Hitachi) and trade groups (e.g., JEIDA 
\[Japan Electronic Industry Development 
Association\]) have sent teems to visit MT projects 
around the world and assess the state of the art. 
University researchers have been given sabbaticals 
to work at Western MT centers (e.g., Shudo at 
Texas, Tsujii at Grenoble). Other representatives 
have indicated Japan's desire to participate in the 
CEC's EUROTRA project \[Margaret King, personal 
communication\]. Japan evidences a long-term, 
growing commitment to acquire and develop HT 
technology. The Japanese leadership is convinced 
that success in MT is vital to their future. 
METAL 
Of the major MT R&D groups around the world, it 
would appear that the new METAL project at the 
Linguistics Research Center of the University of 
Texas is closest to delivering a product. The 
METAL German-English system passed tests in a 
production-style setting in late 1982, mid-EJ, and 
early 1984, and the system has been installed at 
the sponsor's site in Germany for further testing 
and final development of a translator interface. 
The METAL dictionaries are being expanded for 
maximum possible coverage of selected technical 
areas in anticipation of production use in 1984. 
Commercial introduction is also a possibility. 
Work on other language pairs has begun: 
English-German is now underwayj and Spanish and 
Chinese are in the target language design stage. 
One of the particular strengths of the METAL system 
is its accommodation of a variety of linguistic 
theories/strategies. The German analysis component 
is based on a context-free phrase-structure 
grammar, augmented by procedures with facilities 
ford among other things, arbitrary transformations. 
The English analysis component, on the other hand, 
employs a modified GPSG approach and makes no use 
of transformations. Analysis is completely 
separated from transfer, and the system is 
multi-lingual in that a given constituent structure 
analysis can be used for transfer and synthesis 
into multiple target languages. Experimental 
translation of English into Chinese (in addition to 
German) will soon be underway; translation from 
both English and German into Spanish is expected to 
begin in the immediate future. 
555 
The transfer component of METAL includes two 
transformation packages, one used by transfer 
grammar rules and the other by transfer dictionary 
entries; these co-operate during transfer, which is 
effected during a top-down exploration of the 
/highest-scoring\] tree produced in the analysis 
phase. The strategy for the top-down pass is 
controlled by the linguist who writes the transfer 
rules; these in turn are paired i-I with the 
grammar rules used to perform the original 
analysis, so that there is no need to search 
through a general transfer gr-m,,-r to find 
applicable rules (potentially allowing application 
of the wrong ones). As implied above, structural 
and lexical transfer are performed in the same 
pass, so that each may influence the operation of 
the other; in particular, transfer dictionary 
entries may specify the syntactic and/or semantic 
contexts in which they are valid. If no analysis 
is achieved for a given input, the longest phrases 
which together span that input are selected for 
independent transfer and synthesis, so that every 
input (a sentence, or perhaps a phrase) results in 
some translation. 
In addition to producing a translation system per 
se, the Texas group has developed software packages 
for text processing (so as to format the output 
translations like the original input documents), 
data base management (of dictionary entries and 
grammar rules), rule validation (to eliminate most 
errors in dictionary entries and gr-,-m-r rules), 
dictionary construction (to enhance human 
efficiency in coding lexical entries)j etc. Aside 
from the word-processing front-end (being developed 
by Siemens, the project sponsor), the METAL group 
is developing a complete system, rather than a 
basic machine translation engine that leaves much 
drudgery for its human developers/users. Lehmann 
et al. \[81\], Bennett \[82\], and Slocum \[83, 84\] 
present more details about the METAL system. 
GETA 
As discussed earlier, the Groupe d'Etudes pour la 
Traduction Automatique was formed when Grenoble 
abandoned the CETA system. In reaction to the 
failures of the interlingua approach, GETA adopted 
the transfer approach. In addition, the former 
software design was largely discarded, and a new 
software package supporting a new style of 
processing was substituted. The core of GETA is 
composed of three programs: one converts strings 
into trees (for, e.g., word analysis), one converts 
trees into trees (for, e.g., syntactic analysis and 
transfer), and the third converts trees into 
strings (for, e.g., word synthesis). The overall 
translation process is composed of a sequence of 
stages, wherein each stage employs one of these 
three programs. 
One ot the features of GETA that sets it apart from 
other MT systems is the insistence on the part of 
the designers that no stage be more powerful than 
is minimally necessary for its proper function. 
Thus, rather than supplying the linguist with 
programming tools capable of performing any 
operation whatever (e.g., the arbitrarily powerful 
Q-systems of TAUM), GBTA supplies at each stage 
only the minimum capability necessary to effect the 
desired linguistic operation, and no more. This 
reduces the likelihood that the linguist will 
become overly ambitious and create unnecessary 
problems, and also enabled the programmers to 
produce software that runs more rapidly than would 
be possible with a more general scheme. 
A "grammar" in GETA is actually a network of 
subgrammars; that is, a grammar is a graph 
specifying alternative sequences of applications of 
the subgr---,-rs and optional choices of which 
subgra~mars are to be applied (at all). The 
top-level grammar is therefore a "control graph" 
over the subgrm, m-rs which actually effect the 
linguistic operations -- analysis, transfer, etc. 
GETA is sufficiently general to allow 
implementation of any linguistic theory, or even 
multiple theories at once (in separate subgrammars) 
if such is desired. Thus, in principle, GETA is 
completely open-ended and could accommodate 
arbitrary semantic processing and reference to 
"world models" of any description. 
In practice, however, the story is more 
complicated. In order to increase the 
computational flexibility, as is required to take 
advantage of substantially new linguistic theories, 
especially "world models', the underlying software 
would have to be changed in many various ways. 
Unfortunately, it is written in IBM assembly 
language, making modification extremely difficult. 
Worse, the programmers who wrote the software have 
long since left the GETA project, and the current 
staff is unable to safely attempt significant 
modification. As a result, there has been no 
substantive change to the GETA software since 1975, 
and the GBTA group has been unable to experiment 
with any new computational strategies. Back-up, 
for example, is a known problem \[Tsujii, personal 
communication\]: if the GETA system "pursues a wr6ng 
path" through the control graph of subgr~mmars, it 
can undo some of its work by backing up past whole 
graphs, discarding the results produced by entire 
subgr---,-rs; but within a subgr-mm-r, there is no 
possibility of backing up and reversing the effects 
of individual rule applications. The GETA workers 
would like to experiment with such a facility, but 
are unable to change the software to allow this. 
Until GETA receives enough funding that new 
progra~mers can be hired to rewrite the software in 
a high-level language, facilitating present and 
future redesign, the GETA group is "stuck" with the 
current software, now 10 years old and showing 
clear signs of age, to say nothing of 
non-transportability. 
GETA seems not to have been pressed to produce an 
application early on, and the staff was relatively 
"free" to pursue research interests. Until GETA 
can be updated, and in the process freed from 
dependence on IBM mainframes, it may never he a 
viable system. The project staff are actively 
seeking funding for such a project. Meanwhile, the 
French goverr=nent has launched an application 
effort through the GETA group. 
556 
SUSY - Saarbruecker Uebersetzungssystem 
The University of the Saar at Saarbruecken, West 
Germany, hosts one of the larger MT projects in 
Europe, established in the late 1960"s. After the 
failure of a project intended to modify GAT for 
Russian-German translation, a new systsm was 
designed along somewhat similar lines to translate 
Russian into German after "global" sentence 
analysis into dependency tree structures, using the 
transfer approach. Unlike most other F?r projects, 
the Saarbruecken group was left relatively free to 
pursue research interests, rather than forced to 
produce applications, and was also funded at a 
level sufficient to permit significant on-going 
experimentation and modification. As a result, 
SUSY tended to track external developments in ~ 
and AI more closely than other projects. For 
example, Saarbruecken helped establish the 
co-operative HT group LEIBNIZ (along with Grenoble 
and others) in 1974, and adopted design ideas from 
the GETA system. Until 1975, SUSY was based on a 
strict transfer approach; since 1976, however, it 
has evolved, becoming more abstract as linguistic 
problems mandating "deeper" analysis have forced 
the transfer representations to assume some of the 
generality of an interlingua. Also as a result of 
such research freedom, there was apparently no 
sustained attempt to develop coverage for specific 
applications. 
Intended as a multi-lingual system involving 
English, French, German and Russian, work on SUSY 
has concentrated on translation into German from 
Russian and, recently, English. Thus, the extent 
to which SUSY may be capable of multilingual 
translation has not yet been ascertained. Then, 
toO, some aspects of the software are surprisingly 
primitzve: only very recently, for example, did the 
morphological analysis program become 
nondeterministic (i.e., general enough to permit 
lexical ambiguity). The strongest limiting factor 
in the further development of SUSY seems to be 
related to the initial inspiration behind the 
project: SUSY adopted a primitive approach in which 
the linguistic rules were organized into 
independent strata, and were incorporated directly 
into the software \[Maas, 84\]. As a consequence, 
the rules were virtually unreadable, and their 
interactions, eventually, became almost impossible 
to manage. In terms of application potential, 
therefore, SUSY seems to have failed. A 
second-generation project, SUSY-II, begun in 1981, 
may fare better. 
EUROTRA 
EUROTRA is the largest MT project in the Western 
world. It is the first serious attempt to produce 
a true multi-lingual system, in this case intended 
for all seven European Economic Community 
languages. The justification for the project is 
simple, inescapable economics: over a third of the 
entire administrative budget of the EEC for 1982 
was needed to pay the translation division (average 
individual income: $43,O00/year), which still could 
not keep up with the demands placed on it; 
technical translation costs the EEC $.20 per word 
for each of six translations (from the seventh 
original language), and doubles the cost of the 
technology documented; with the addition of Spain 
and Portugal later this decade, the translation 
staff would have to double for the current demand 
level (unless highly productive machine aids were 
already in place) \[Perusse, 83\]. The high cost of 
writing SYSTRAR dictionary entries is presently 
justifiable for reasons of speed in translation, 
but this situation is not viable in the long term. 
The EEC must have superior quallty MT at lower cost 
for dictionary work. Human translation alone will 
never suffice. 
EUROTRA is a true multi-national development 
project. There is no central laboratory where the 
work will take place, but instead designated 
University representatives of each member country 
will produce the analysis and synthesis modules for 
their native language; only the transfer modules 
will be built by a "central" group -- and the 
transfer modules are designed to be as small as 
possible, consisting of little more than lexical 
substitution \[King, 82\]. Software development will 
be almost entirely separated from the linguistic 
rule development; indeed, the production software, 
though designed by the EUROTRAmembers, will be 
written by whichever commercial software house wins 
the contract in bidding competition. Several 
co-ordinating c~ittees are working with the 
various language and emphasis groups to insure 
co-operation. 
The linguistic basis of EUROTRA is nothing novel. 
The basic structures for representating "meaning" 
are dependency trees, marked with feature-value 
pairs partly at the discretion of the language 
groups writing the gram~nars (anything a group 
wants, it can add), and partly controlled by mutual 
agreement among the language groups (a certain set 
of feature-value combinations has been agreed to 
constitute minimum information; all are constrained 
to produce this set when analyzing sentences in 
their language, and all may expect it to be present 
when synthesizing sentences in their language) 
\[King, 81, 82\]. 
The software basis of EUROTRA will not be novel 
either, though the design is not yet complete. The 
basic rule interpreter will be "a general re-write 
system with a control language over 
grazamars/processes" \[King, personal communication\]. 
As in GETA, the linguistic rules will be bundled 
into packets of subgrammars, and the linguists will 
be provided with a means of controlling which 
packets of rules are applied, and when; the 
individual rules will be non-destructive re-write 
rules, so that the application of any given rule 
may create new structure, but will never erase any 
old information (no back-up). 
EUROTRAwill engage in straightforward development 
using state-of-the-art but "proven" techniques. 
The charter requires delivery of a small 
representative prototype system by late 1987, and a 
prototype covering one technical area by late 1988. 
EUROTRA is required to translate among the native 
languages of all member countries which sign the 
"contract of association" by early mid-84; thus, 
not all seven EEC languages will necessarily be 
557 
represented, but by law at least four languages 
must be represented if the project is to continue. 
The State of the Art 
Human languages are, by nature, different. So much 
so, that the illusory goal of abstract perfection 
in translation -- once and still imagined by some 
to be achievable -- can be comfortably ruled out of 
the realm of possible existence, whether attempted 
by machine or man. Even the abstract notion of 
"quality" is undefinable, hence immeasurable. In 
its place, we must substitute the notion of 
evaluation of translation according to its purpose, 
judged by the consomer. One must therefore accept 
the truth that the notion of quality is inherently 
subjective. Certainly there will be translations 
hailed by most if not all as "good," and 
correspondingly there will be translations almost 
universally labelled 'bad." Most translations, 
however, will surely fall in between these 
extremes, and each user must render his own 
judgement according to his needs. 
In corporate circles, however, there is and has 
always been an operational definition of "good" vs. 
'bad" translation: a good translation is what 
senior translators are willing to expose to outside 
scrutiny (not that they are fully satisfied, for 
they never are); and a bad one is what they are not 
willing to release. These experienced translators 
-- usuatly post-editors -- impose a judgement which 
the corporate body is willing to accept at face 
value: after all, such judgement is the very 
purpose for having senior translators. It is 
arrived at subjectively, based on the purpose for 
which the translation is intended, but comes as 
close to being an objective assessment as the world 
is likely to see. In a post-edltin@ context, a 
"good" original translation is one worth revising 
i.e., one which the editor will endeavor to 
change, rather than reject or replace with his own 
original translation. 
Therefore, any rational position on the state of 
the art in MT & MAT must respect the operational 
decisions about the quality of MT & MAT as judged 
by the present users. These systems are all, of 
course, based on old technology ("ancient," by the 
standards of AI researchers); but by the time 
systems employing today's AI technology hit the 
market, they too will be "antiquated" by the 
research laboratory standards of their time. Such 
is the nature of technology. We will therefore 
distinguish, in our assessment, between what is 
available and/or used now ("old," yet operationally 
current, technology), and what is around the next 
corner (techniques working in research labs today), 
and what is farther down the road (experimental 
approaches). 
Productlon Systems 
Production M(A)T systems are based on old 
technology; some, for example, still (or until very 
recently did) employ punch-cards and print(ed) out 
translations in all upper-case. Few if any attempt 
a comprehensive "global" analysis at the sentence 
level (trade secrets make this hard to discern), 
and none go beyond that to the paragraph level. 
None use a significant amount of semantic 
information (though all claim to use some). Most 
if not all perform as "idiots savants', making use 
of enormous amounts of very unsophisticated 
pragmatic information and brute-force computation 
to determine the proper word-for-word or 
idiom-for-idiom translation followed by local 
rearrangement of word order -- leaving the 
translation chaotic, even if understandable. 
But they work! Some of them do, anyway -- well 
enough that their customers find reason to invest 
enormous amounts of time and capital developing the 
necessary massive dictionaries specialized to their 
applications. Translation time is certainly 
reduced. Translator frustration is increased or 
decreased, as the case may be (it seems that 
personality differences, among other things, have a 
large bearing on this). Some translators resist 
their introduction -- there are those who still 
resist the introduction of typewriters, to say 
nothing of word processors -- with varying degrees 
of success. But most are thinking about accepting 
the place of computers in translation, and a few 
actually look forward to relief from much of the 
drudgery they now face. Current MT systems seem to 
take some getting used to, and further productivity 
increases are realized as time goes by; they are 
usually accepted, eventually, as a boon to the 
bored translator. New products embodying old 
technology are constantly introduced; most are 
found not viable, and quickly disappear from the 
market. But those which have been around for years 
must be economically justifiable to their users -- 
else, presumably, they would no longer exist. 
Development Systems 
Systoms being developed for near-term introduction 
employ Computational Linguistics (CL) techniques cf 
the late 1970"s, if not the 80"s. Essentially all 
are full HT, not MAT, systems. As Hutchins \[82\] 
notes, "...there is now considerable agreement on 
the basic strategy, i.e. a "transfer" system with 
some semantic analysis and some interlingual 
features in order to simplify transfer components." 
These systems employ one of a variety of 
sophisticated parsing/transducing techniques, 
typically based on charts, whether the grammar is 
expressed via phrase-structure rules (e.g., METAL) 
or \[strings of\] trees (e.g., GETA, EUROTRA); they 
operate at the sentence level, or higher, and make 
significant use of semantic features. Proper 
linguistic theories, whether elegant or not quite, 
and heuristic software strategies take the place of 
simple word substitution and brute-force 
programming. If the analysis attempt succeeds, the 
translation stands a fair chance of being 
acceptable to the revisor; if analysis fails, then 
fail-soft measures are likely to produce something 
equivalent to the output of a current production MT 
system. 
These systems work well enough in experimental 
settings to give their sponsors and waiting 
customers (to say nothing of their implementors) 
reason to hope for near-term success in 
application. Their technology is based on some of 
558 
the latest techniques which appear to be workable 
in i,m, ediate large-scale application. Most "pure 
AI" techniques do not fall in this category; thus, 
serious AI researchers look down on these 
development systems (to say nothing of production 
systems) as old, uninteresting -- and probably 
useless. Some likely are. But others, though 
"old," will soon find an application niche, and 
will begin displacing any of the current production 
systems which try to compete. (Since the present 
crop of development systems all seen to be aimed at 
the "information dissemination" application, the 
current productlon systems that are aimed at the 
"information acquisition" market may survive for 
some time.) The major hurdle is time: time to 
write and debug the grammars (a very hard task), 
and time to develop lexicons with roughly ten 
thousand general vocabulary items, and the few tens 
of thousands of technical terms required per 
subject area. Some development projects have 
invested the necessary time, and stand ready to 
deliver commercial applications (e.g., GETA, 
METAL). 
Research Systems 
The biggest problem associated with MT research 
systems is their scarcity (nonexistence, in the 
U.S.). If current CL and AI researchers were 
seriously interested in multiple languages -- even 
if not for translation per se -- this would not 
necessarily be a bad situation. But in the U.S. 
they certainly are not, and in Europe, CL and AI 
research has not yet reached the level achieved in 
the U.S. Western business and industry are 
naturally more concerned with near-term payoff, and 
some track development systems; very few support FiT 
development directly, and none yet support pure D~ 
research at a significant level. (The Dutch firm 
Philips may, indeed, have the only long-term 
research project in the West.) Some European 
governments fund significant R&D projects (e.g., 
Germany and France), but Japan is making by far the 
world's largest investment in MT research. The 
U.S. government, which otherwise supports the best 
overall AI and \[English\] CL research in the world, 
is not involved. 
Where pure MT research projects do exist, they tend 
to concentrate on the problems of deep meaning 
representations -- striving to pursue the goal of a 
true AI system, which would presumably include 
language-independent meaning representations of 
great depth and complexity. Translation here is 
seen as just one application of such a system: the 
system "understands" natural language input, then 
"generates" natural language output; if the 
languages happen to be different, then translation 
has been performed via paraphrase. Translation 
could thus be viewed as one of the ultimate tests 
of an Artificial Intelligence: if a system 
"translates correctly," then to some extent it can 
be argued to have "understood correctly," and in 
any case will tell us much about what translation 
is all about. In this role, MT research holds out 
its greatest promise as a once-again scientifically 
respectable discipline. The first requirement, 
however, is the existence of research groups 
interested in, and funded for, the study of 
multiple languages and translation among them 
within the framework of AI research. At the 
present time only Japan, and to a somewhat lesser 
extent western Europe, can boast such groups. 
Future Prospects 
The world has changed in the two decades since 
ALPAC. The need and demand for technical 
translation has increased dramatically, and the 
supply of qualified human technical translators has 
not kept pace. (Indeed, it is debatable whether 
there existed a sufficient supply of qualified 
technical translators even in 1966, contrary to 
ALPAC's claims.) The classic "law of supply and 
demand" has not worked in this instance, for 
whatever reasons: the shortage is real, all over 
the world; nothing is yet serving to stem this 
worsening situation; and nothing seems capable of 
doing so outside of dramatic productivity increases 
via computer automation. In the EEC, for example, 
the already overwhelming load of technical 
translation is projected to rise sixfold within 
five years. 
The future premises greater acceptance by 
translators of the role of machine aids -- running 
the gamut from word processing systems and on-line 
term banks to MT systems -- in technical 
translation. Correspondingly, M(A)T systems will 
experience greater success in the marketplace. As 
these systems continue to drive down the cost of 
translation, the demand and capacity for 
translation will grow even more than it would 
otherwise: many "new" needs for translation, not 
presently economically justifiable, will surface. 
If MT systems are to continue to improve so as to 
further reduce the burden on human translators, 
there will be a greater need and demand for 
continuing MT R&D efforts. 
Conclusions 
The translation problem will not go away, and human 
solutions (short of full automation) do not now, 
and never will, suffice. MT systems have already 
scored successes among the user community, and the 
trend can hardly fail to continue as users demand 
further improvements and greater speed, and MT 
system vendors respond. Of course, the need for 
research is great, but some current and future 
applications will continue to succeed on economic 
grounds alone -- and to the user community, this is 
virtually the only measure of success or failure. 
It is important to note that translation systems 
are not going to "fall out" of AI efforts which are 
not seriously contending with multiple languages 
from the start. There are two reasons for this. 
First, English is not a representative language. 
Relatively speaking, it is not even a very hard 
language from the standpoint of Computational 
Linguistics: Japanese, Chinese, Russian, and even 
German, for example, seem more difficult to deal 
with using existing CL techniques -- surely in pert 
due to the nearly total concentration of CL workers 
on English. Developing translation ability will 
require similar concentration by CL workers on 
other languages; nothing less will suffice. 
559 
Second, it would seem that translation is not by 
any means a simple matter of understanding the 
source text, then reproducing it in the target 
language -- even though some translators (and 
virtually every layman) will say this is so. On 
the one hand, there is the serious question of 
whether, in for example the case of an article on 
front-line research in semiconductor switching 
theory, or nuclear physics, a translator really 
does "fully comprehend" the content of the article 
he is translating. One would suspect not. 
(Johnson \[83\] makes a point of claiming that he has 
produced translations, judged good by informed 
peers, in technical areas where his expertise is 
deficient, and his understanding, incomplete.) On 
the other hand, it is also true that translation 
schools expend a great deal of effort teaching 
techniques for low-level lexical and syntactic 
manipulation -- a curious fact to contrast with the 
usual "full comprehension" claim. In any event, 
every qualified translator will agree that there is 
much more to translation than simple 
analysis/synthesis (an almost prima facie proof of 
the necessity for Transfer). 
What this means is that the development of 
translation as an application of Computational 
Linguistics will require substantial research in 
its own right in addition to the work necessary in 
order to provide the basic multi-lingual analysis 
and synthesis tools. Translators must be 
consulted, for they are the experts in translation. 
None of this will happen by accident; it must 
result from design. 
References 
Amano, S. Machine Translation Project at Toshiba 
Corporation. Technical note. Toshiba Corporation, 
R&D Center, Information Systems Laboratory, 
Kawasaki, Japan, November 1982. 
Bar-Hillel, Y., "Some Reflections on the Present 
Outlook for High-Quality Machine Translation," in 
W.P. Lehmann and R. Stachowitz (eds.), 
Feasibility Study on Fully Automatic High Quality 
Translation. Final technical report 
RADC-TR-71-295. Linguistics Research Center, 
University of Texas at Austin, December 1971. 
Bennett, W. S. The Linguistic Component of METAL. 
Working paper LRC-82-2, Linguistics Research 
Center, University of Texas at Austin, July 1982. 
Bostad, D. A., 'Quality Control Procedures in 
Modification of the Air Force Russian-EnKlish MT 
System," in V. Lawson (ed.), Practical Experience 
of Machine Translation. North-Holland, Amsterdam, 
1982, pp. 129-133. 
Bruderer, H. E., "The Present State of Machine and 
Machine-Assisted Translation," in Commission of the 
European Communities, Third European Congress on 
Information Systems and Networks: Overcoming the 
Language Barrier, vol. i. Verlag Dokumentation, 
Munich, 1977, pp. 529-556. 
Gervais, A., et DG de la Planification, de 
l'Evaluation et de la Verification. Rapport final 
d'~valuation du syst~me pilots de traduction 
automatique TAUM-AVIATION. Canada, Secretariat 
d'Etat, June 1980. 
Hundt, M. G., 'Working with the Weidner 
Machine-Aided Translation System," in V. Lawson 
(ed.), Practical Experience of Machine Translation. 
North-Holland, Amsterdam, 1982, pp. 45-51. 
Hutchins, W. J., "Progress in Documentation: 
Machine Translation and Machine-Aided Translation," 
Journal of Documentation 34, 2, June 1978, pp. 
119-159. 
Hutchins, W. J., "The Evolution of Machine 
Translation Systems," in V. Lawson (ed.), 
Practical Experience of Machine Translation. 
North-Holland, Amsterdam, 1982, pp. 21-37. 
Johnson, R. L., "Parsing - an MT Perspective," in 
K. S. Jones and Y. Wilks (eds.), Automatic Natural 
Language Parsing. Ellis Horwood, Ltd., Chichester, 
Great Britain, 1983. 
Jordan, S. R., A. F. R. Brown,, and F. C. Rutton, 
"Computerized Russian Translation at ORNL," in 
Proceedings of the ASIS Annual Meeting, San 
Francisco, 1976, p. 163; also in ASIS Journal 28, 
1, 1977, pp. 26-33. 
King, M., '~esign Characteristics of a Machine 
Translation System," Proceedings of the Seventh 
IJCAI, Vancouver, B.C., Canada, Aug. 1981, vol. I, 
pp. 43-46. 
King, M., "EUROTRA: An Attempt to Achieve 
Multilingual MT," in V. Lawson (ed.), Practical 
Experience of Machine Translation. North-Holland, 
Amsterdam, 1982, pp. 139-147. 
Lehmann, W. P., W. S. Bennett, J. Sloeum, H. Smith, 
S. M. V. Pfluger, and S. A. Eveland. The METAL 
System. Final technical report RADC-TR-80-374, 
Linguistics Research Center, University of Texas at 
Austin, January 1981. NTIS report A0-97896. 
Loh, S.-C., '~achine Translation: Past, Present, 
and Future," ALLC Bulletin 4, 2, March 1976, pp. 
105-114. 
Lob, S.-C., L. KonE, and H.-S. Rung, '~achine 
Translation of Chinese Mathematical Articles," ALLC 
Bulletin 6, 2, 1978, pp. 111-120. 
Loh, S.-C., and L. Kong, "An Interactive On-Line 
Machine Translation System (Chinese into English)," 
in B. M. Snell (ed.), Translating and the Computer. 
North-Holland, Amsterdam, 1979, pp. 135-148. 
Lytle, E. G., D. Packard, D. Gibb~ A. g. Melby, and 
F. H. Billings, "Junction Grammar as a Base for 
Natural Language Processing," AJCL 3, 1975, 
microfiche 26, pp. 1-77. 
Maas, H.-D., "The D~ system SUSY," presented at the 
ISSCO Tutorial on Machine Translation, Lugano, 
Switzerland, 2-6 April 1984. 
560 
Melby, A. K. "Multi-level Translation Aids in a 
Distributed System," Ninth ICCL \[COLING 82\], 
Prague, Czechoslovakia, July 1982, pp. 215-220. 
Muraki, K., and S. Ichiyama. An Overview of Machine 
Translation Project at NEC Corporation. Technical 
note. NEC Corporation, C & C Systems Research 
Laboratories, 1982. 
Nagao, H., J. Tsujii, K. Mitamure, N. Rirakawa, and 
M. Kume, "A Machine Translation System from 
Japanese into English: Another Perspective of MT 
Systems," Proceedings of the Eighth ICCL \[COLING 
80\], Tokyo, 1980, pp. 414-423. 
Nagao, M., et el. On English Generation for a 
Japanese-English Translation System. Technical 
Report on Natural Language Processing 25. 
Information Processing of Japan, 1981. 
Nagao, M., J. Tsujii, K. Yada, and T. Kakimoto, "An 
English Japanese Machine Translation System of the 
Titles of Scientific and Engineering Papers," 
Proceedings of the Ninth ICCL \[COLING 82\], Prague, 
5-10 July 1982, pp. 245-252. 
Nishida, F., and S. Takamatsu, 'Uapanese-English 
Translation Through Internal Expressions," 
Proceedings of the Ninth ICCL \[COLING 82\], Prague, 
5-10 July 1982, pp. 271-276. 
Nisnida, T., and S. Doshita, '~n English-Japanese 
Machine Translation System Based on Formal 
Semantics of Natural Language," Proceedings of the 
Ninth ICCL \[COLING 82\], Prague, 5-10 July 1982, pp. 
277-282. 
Nisnida, T., and S. Doshita. An Application of 
Montague Grammar to English-Japanese }~chine 
Translation. Proceedings of the ACL-NRL Conference 
on Applied Natural Language Processing, Santa 
Monica, California, February 1983, pp. 156-165. 
Nomura, H., and A. Shimazu. Machine Translation in 
Japan. Technical note. Nippon Telegraph and 
Telephone Public Corporation, Musashino Electrical 
Communication Laboratory, Tokyo, November 1982. 
Nomura, N., A. Shimazu, H. Iida, Y. Katagiri, Y. 
Saito, S. Naito, K. Ogura, A. Yokoo, and M. 
Mikami. Introduction to LUTE (Language 
Understander, Translator & Editor). Technical 
note, Musashino Electrical Communication 
Laboratory, Research Division, Nippon Telegraph and 
Telephone Public Corporation, Tokyo, November 1982. 
Perusse, D., '~achine Translation," ATA Chronicle 
12, 8, 1983, pp. 6-8. 
Pigott, I. M., "Theoretical Options and Practical 
Limitations of Using Semantics to Solve Problems of 
Natural Language Analysis and Machine Translation," 
in H. MacCatferty and K. Gray (eds.), The Analysis 
of Meaning: Informatics 5. ASLIB, London, 1979, 
pp. 239-268. 
Pigott, I. M., "The Importance of Feedback from 
Translators in the Development of High-Quality 
Machine Translation," in V. Lawson (ed.), Practical 
Experience of Machine Translation. North-Holland, 
Amsterdam, 1982, pp. 61-73. 
Ruffino, J. R., "Coping with Machine Translation," 
in V. Lawson (ed.), Practical Experience of Machine 
Translation. North-Holland, Amsterdam, 1982, pp. 
57-60. 
Sawai, S., R. Fukushima, M. Sugimoto, and N. Ukai, 
'~nowledge Representation and Machine Translation," 
Proceedings of the Ninth ICCL \[COLING 82\], Prague, 
5-10 July 1982, pp. 351-356. 
Sereda, S. P., "Practical Experience of Machine 
Translation," in V. Lawson (ed.), Practical 
Experience of Machine Translation. North-Holland, 
Amsterdam, 1982, pp. 119-123. 
Shudo, K., "On Machine Translation from Japanese 
into English for a Technical Field," Information 
Processing in Japan 14, 1974, pp. 44-50. 
Sinaiko, S. W., and G. R. Klare, '~urther 
Experiments in Language Translation: A Second 
Evaluation of the Readability of Computer 
Translations," ITL 19, 1973, pp. 29-52. 
Slocu~, J. '~ Status Report on the LRC Machine 
Translation System," Proceedings of the ACL-NRL 
Conference on Applied Natural Language Processing, 
Santa Monica, California, I-3 February 1983, pp. 
166-173. 
Slocu~, J., '~ETAL: The LRC Machine Translation 
System," presented at the ISSCO Tutorial on Machine 
Translation, Lugano, Switzerland, 2-6 April 1984. 
Thouin, B., "The Meteo System," in V. Lawson (ed.), 
Practlcal Experience of Machine Translation. 
North-Holland, Amsterdam, 1982, pp. 39-44. 
Tsujii, J., "The Transfer Phase in an English- 
Japanese Translation System," Proceedings of the 
Ninth ICCL \[COLING 82\], Prague, 5-10 July 1982, pp. 
383-390. 
Van Slype, G., '~valuation du syst~me de traduction 
automatlque SYSTE~ anglais-fran~ais, version 1978, 
de la Commission des communaut~s Europ~ennes," 
Babel 25, 3, 1979, pp. 157-162. 
Van Slype, G., "Economic Aspects of Machine 
Translation," in V. Lawson (ed.), Practical 
Experience of Machine Translation. North-Holland, 
Amsterdam, 1982, pp. 79-93. 
Vasconcellos, M., '~Lanagoment of the Machine 
Translation Envirooment," Proceedings of the ASLIB 
Conference on Translating and the Computer, London, 
November 1983. 
Wheelerp P., "The Errant Avocado (Approaches to 
Ambiguity in SYSTEAN Translation)," Newsletter 13, 
Natural Language Translations Specialist Group, 
BCS, February 1983. 
Wilks, Y., and LATSEC, Inc. Comparative 
Translation Quality Analysis. Final report on 
contract F-33657-77-C-0695. 1978. 
561 
