A SURVEY OF MACHINE TRANSLATION: 
ITS HISTORY, CURRENT STATUS, 
AND FUTURE PROSPECTS 
Jonathan Sloculn 
Microelectronics and Computer 
Technology Corporation 
Austin, Texas 
Elements of 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 
Machine Translation (MT) of natural human languages is 
not a subject about which most scholars feel neutral. 
This field has had a long, colorful career, and boasts no 
shortage of vociferous detractors and proponents alike. 
During its first decade in the 1950s, interest and support 
was fueled by visions of high-speed high-quality trans- 
lation 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 
1960s, disillusionment crept in as the number and diffi- 
culty of the linguistic problems became increasingly obvi- 
ous, 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 alike. 
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 infor- 
mation from foreign \[Russian\] sources so quickly; in 
addition, private companies were developing and selling 
MT systems based on the mid-60s technology so roundly 
castigated by ALPAC. Nevertheless the general disrepute 
of MT resulted in a remarkably quiet third decade. 
We are now into the fourth decade of MT, and there is 
a resurgence of interest throughout the world - plus a 
growing number of MT and MAT (Machine-aided Trans- 
lation) systems in use by governments, business and 
industry: in 1984 approximately half a million pages of 
text were translated by machine. 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 government 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 capa- 
bilities of the newer MT systems lie well beyond what 
was possible just one decade ago. 
In light of these events, it is worth reconsidering the 
potential of, and prospects for, Machine Translation. 
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Computational Linguistics, Volume 11, Number 1, January-March 1985 1 
Jonathan Slocum A Survey of Machine Translation 
After opening with an explanation of how \[human\] trans- 
lation is done where it is taken seriously, we present a 
brief introduction to MT technology and a short historical 
perspective before considering the ,present status arid 
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 west- 
ern Europe, though some other MT projects and less-am- 
bitious 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. Thus, there 
are college-level and post-graduate schools that teach the 
theory (translatology) as well as the practice of trans- 
lation; 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 trans- 
lation. (This 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-na- 
tional firm Siemens, even internal communications that 
are translated are post-edited. Such news generally comes 
as a surprise, if not a shock, to most people in the U.S. 
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 situ- 
ation (Bar-Hillel 1971). So an MT system does not have 
to print and bind the result of its translation in order to 
qualify as "fully automatic". "High 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 under- 
stand, 
• first, the broad categories into which they can be clas- 
sified; 
• second, the different purposes '. for which translations 
(however produced) are used; 
• third, the intended appfications of these systems; and 
• fourth, something about the linguistic techniques MT 
systems employ in attacking the translation problem. 
CATEGORIES OF SYSTEMS 
There are three broad categories of computerized trans- 
lation tools (the differences hinging on how ambitious the 
system is intended to be): Machine Translation (MT), 
Machine-aided Translation (MAT), and Terminology 
Data banks. 
MT systems are intended to perform translation with- 
out human intervention. This does not rule out pre-pro- 
cessing (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). Howev- 
er, an MT 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 MT system). MT occupies the top range 
of positions on the scale of computer translation ambi- 
tion. 
MAT systems fall into two subgroups: human-assisted 
machine translation (HAMT) and machine-assisted 
human translation (MAHT). These occupy successively 
lower ranges on the scale of computer translation ambi- 
tion. HAMT 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. MAHT 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 or 
2 Computational Linguistics, Volume 11, Number 1, January-March 1985 
Jonathan Slocum A Survey of Machine Translation 
thesaurus, accessing a remote terminology data bank, 
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 
MA(H)T system (the system does not need help, instead, 
it is making help available), but post-editing is frequently 
appropriate. 
Terminology Data banks (TD) are the least ambitious 
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 trans- 
lation. Indeed the data bank 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 auto- 
mated (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 users. 
THE PURPOSES OF TRANSLATION 
The most immediate division of translation purposes 
involves information acquisition versus dissemination. 
The classic example of the former purpose is intelli- 
gence-gathering: with masses of data to sift through, 
there is no time, money, or incentive to carefully trans- 
late every document by 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 inexpen- 
sive means of translation were available, then - for texts 
within the reader's areas of expertise - even a low-quali- 
ty translation might be sufficient for information acquisi- 
tion. 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 trans- 
lation 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 usual- 
ly 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 such 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 b¢ "right" as well as clear. Qualified 
human technical translators are hard to find, expensive, 
and slow (translating somewhere around 4 to 6 pages per 
day, on the average). The information dissemination 
application is most 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; more- 
over, the acquisition, maintenance, and consistent use of 
valid technical terminology is an enormous problem. 
Worse, in many technical fields there is a distinct short- 
age 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 productiv- 
ity through computer technology: full-scale MT, less 
ambitious MAT, on-line terminology data banks, 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 fideli- 
ty to content (especially for poetry). ~ In technical trans- 
lation, 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 trans- 
lation. 
LINGUISTIC TECHNIQUES 
There are several perspectives from which one can view 
MT techniques. We will use the following: direct versus 
indirect; interlingua versus transfer; and local versus 
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 obvi- 
ous reasons, so this is no longer a distinguishing charac- 
teristic. 
Direct translation is characteristic of a system (e.g., 
GAT) designed from the start to translate out of one 
specific language and into another. Direct systems are 
Computational Linguistics, Volume 11, Number 1, January-March 1985 3 
Jonathan Slocum A Survey of Machine Translation 
limited to the minimum 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, disambiguation is performed to 
the extent necessary to determine the "meaning" 
(however represented) of the source language input, irre- 
spective of which target language(s) that input might be 
iranslated 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 represen- 
tation 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 
grammatical 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 grammatical unit (e.g., sentence) differs 
depending on the language from which it was derived or 
into which it is to be generated; this implies the existence 
of a third translation stage which maps one language-spe- 
cific meaning representation into another: this stage is 
called Transfer. Thus, the overall transfer transhttion 
process is Analysis followed by Transfer and then 
Synthesis. The transfer versus interlingua difference is 
not applicable to all systems; in particular, direct MT 
systems use neither the transfer nor the interlingua 
approach, since they do not attempt to represent 
"meaning". 
Local scope versus global scope is not so much a differ- 
ence of category as degree. Local 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 that 
differ in part of speech and/or derivational history \[thus 
meaning\], but that are written alike) are a major prob- 
lem, because a 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 deter- 
mined by its context within a unified analysis of the 
sentence (or, rarely, paragraph). In such systems, by 
contrast, homographs do not typically constitute a signif- 
icant 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 1977) and MT history (Hutchins 
1978) available in the open literature. To illustrate some 
continuity in the field of MT, while remaining within 
reasonable space limits, our brief historical overview is 
restricted to defunct systems or projects that gave rise to 
follow-on systems or projects of current interest. These 
are: 
• Georgetown's GAT, 
• Grenoble's CETA, 
• Texas's METAL, 
• Montreal's TAUM, and 
• Brigham Young University's ALP system. 
GAT: GEORGETOWN AUTOMATIC TRANSLATION 
Georgetown University was the site of one of the earliest 
MT projects. Begun in 1952, and supported by the U.S. 
government, Georgetown's GAT system became opera- 
tional in 1964 with its delivery to the Atomic Energy 
Commission at Oak Ridge National Laboratory, and to 
Europe's corresponding research facility EURATOM in 
Ispra, Italy. Both systems were used for many years to 
translate Russian physics texts into "English". The 
output quality was quite poor, by comparison with human 
translations, but for the intended purpose of quickly 
scanning documents to determine their content and inter- 
est, the GAT 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 at least 1979 (Jordan et al. 1976, 1977) and 
possibly later. 
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 underly- 
ing 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 trans- 
lation services for which there is no viable alternative to 
even low-quality MT. 
The Georgetown MT project was terminated in the 
mid-60s. Peter Toma, one of the GAT workers, incorpo- 
rated LATSEC and developed the SYSTRAN system, 
which in 1970 replaced the IBM Mark II system at the 
USAF Foreign Technology Division (FTD) at Wright 
4 Computational Linguistics, Volume 11, Number I, January-Marcb 1985 
Jonathan Slocum A Survey of Machine Translation 
Patterson AFB, and in 1976 replaced GAT at EURATOM. 
SYSTRAN is still being used there 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'I~TUDES POUR LA TRADUCTION AUTOMA- 
TIQUE 
In 1961 a project to translate Russian into French was 
started at Grenoble University in France. Unlike GAT, 
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 George- 
town 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 grammatical level, but transfer (implying a 
mapping from one language-specific meaning represen- 
tation 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 (Hutchins 1978). 
The CETA system was under development for ten 
years; during 1967-71 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 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, imme- 
diately followed by the creation of a new project/system 
called GETA, based entirely on a fail-soft transfer design. 
The software included significant amounts of assembly 
language; this continued reliance on assembly language 
was soon to have deleterious effects, for reasons now 
obvious to anyone. We 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 fund- 
ing to establish the Linguistics Research Center, and with 
it the METAL project, to investigate MT - not from 
Russian but from German into English. (MT research at 
the University actually began in 1956.) The LRC 
adopted Chomsky's transformational paradigm, which 
was quickly gaining popularity in linguistics circles, and 
within that framework employed a syntactic interlingua 
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-line, 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 resur- 
rected the project. The FORTRAN program was rewrit- 
ten 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 AG in Munich had 
begun supporting the project, and in 1980 Siemens AG 
became the sole sponsor. 
TAUM: TRADUCTION AUTOMATIQUE DE L'UNIVERSITI~ 
DE MONTRI~AL 
In 1965 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 FORTRAN programming 
language on the CDC 6600 (later, the CYBER" 173). After 
an initial period of more-or-less open-ended research, the 
Canadian government began adopting specific goals for 
the TAUM system. A chance remark by a bored transla- 
tor in the Canadian Meteorological Center (CMC) had 
led to a spin-off project: TAUM-METEO. Weather fore- 
casters already adhered to a relatively consistent 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 fore- 
casts. A prototype was demonstrated in 1976, and by 
1977 METEO was installed for production translation. 
METEO is discussed 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 be started 
by human means, since the result was needed quickly). 
Computational Linguistics, Volume 11, Number 1, January-March 1985 5 
Jonathan Slocum A Survey of Machine Translation 
From this point on, TAUM concentrated on the aviation 
manuals exclusively. To alleviate problems with their 
predominantly syntactic analysis (especially considering 
the many multiple-noun compounds present in the 
aviation manuals), the group began in 1977 to incorpo- 
rate significant semantic analysis in the 
TAUM-AVIATION 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 government 
organized a series of tests and evaluations to assess the 
status of the system. Among other things, it was discov- 
ered that the cost of writing each dictionary entry was 
remarkably high (3.75 man-hours, costing $35-40 Cana- 
dian), and that the system's runtime translation cost was 
also high (6 cents per word) considering the cost of 
human translation (8 cents per word), especially when 
the post-editing costs (10 cents per word for TAUM 
versus 4 cents per word for human translations) were 
taken into account (Gervais 1980). TAUM-AVIATION 
was not yet cost-effective. Several other factors, espe- 
cially 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 original aim was 
to produce a fully-automatic MT system based on Junc- 
tion Grammar (Lytle et al. 1975), but in 1973 the 
emphasis shifted to 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). This Interactive Translation System 
(ITS) performed global analysis of sentences (with human 
assistance), and then indirect transfer (again, with human 
assistance). 
The BYU project never produced an operational 
system (hardware costs and the amount and difficulty of 
human interaction prohibited cost-effectiveness), and the 
Mormon Church, through the University, began to 
dismantle the project. In 1980, a group of BYU program- 
mers joined Weidner Communications Corporation, and 
helped develop the fully-automatic, direct Weidner MT 
system. At about the same time, most of the remaining 
BYU project members left to form Automated Language 
Processing Systems (ALPS) and continue development of 
ITS. Both of these systems are actively marketed today, 
and are discussed in the next section. Some work contin- 
ues at BYU, but at a very much reduced level and degree 
of aspiration (e.g., Melby 1982). 
CURRENT PRODUCTION SYSTEMS 
In this section we consider the major M(A)T systems 
being used and/or marketed today. Some of these origi- 
nate from the "failures" described above, but other 
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 also discuss the MAT systems CULT and ALPS. Most 
of these systems have been installed for several custom- 
ers (METEO, SPANAM, and CULT are the exceptions, 
with only one obvious customer each). The oldest active 
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 (1982) 
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% (1 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 (1982) 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, MT and 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 trans- 
lation. Thus, in the only valid sense of the idiom, MT and 
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. 
6 Computational Linguistics, Volume 11, Number 1, January-March 1985 
Jonathan Sloeum A Survey of Machine Translation 
SYSTRAN 
SYSTRAN was one of the first MT systems to be market- 
ed; the first installation replaced the IBM Mark II 
Russian-English system at the USAF FTD in 1970, and is 
still operational. NASA selected SYSTRAN in 1974 to 
translate materials relating to the Apollo-Soyuz collab- 
oration, and EURATOM replaced GAT with SYSTRAN in 
1976. Also by 1976, FTD was augmenting SYSTRAN 
with word-processing equipment to increase productivity 
(e.g., to eliminate the use of punched cards). The system 
has continued to evolve, for example by the shift toward 
a more modular design and by the allowance of topical 
glossaries (essentially, dictionaries specific to the subject 
area of the text). The system has been argued to be ad 
hoc - particularly in the assignment of semantic features 
(Pigott 1979). The USAF FTD dictionaries number over 
a million entries; Bostad (1982) reports that dictionary 
updating must be severely constrained, lest a change to 
one entry disrupt the activities of many others. (A study 
by Wilks (1978) reported an improvement/degradation 
ratio \[after dictionary updates\] of 7:3, but Bostad implies 
a much more stable situation after the introduction of 
stringent quality-control measures.) 
In 1976 the Commission of the European Communi- 
ties purchased an English-French version of SYSTRAN 
for evaluation and potential use. Unlike the FTD, NASA, 
and EURATOM installations, where the goal was infor- 
mation acquisition, the intended use by CEC was for 
information dissemination - meaning that the output was 
to be carefully edited before human consumption. Van 
Slype (1982) reports that "the English-French standard 
vocabulary delivered by Prof. Toma to the Commission 
was found to be almost entirely useless for the Commis- 
sion environment." Early evaluations were negative 
(e.g., Van Slype 1979), but the existing and projected 
overload on CEC human translators was such that inves- 
tigation 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 English-Italian in 1979). 
The dream of acceptable quality for post-editing 
purposes was eventually realized: Pigott (1982) reports 
that " . . . the enthusiasm demonstrated by \[a few trans- 
lators\] seems to mark something of a turning point in 
\[machine translation\]." Currently, about 20 CEC trans- 
lators in Luxembourg are using SYSTRAN on a Siemens 
7740 computer for routine translation; one factor 
accounting for success is that the English and French 
dictionaries now consist of well over 100,000 entries in 
the very few technical areas for which SYSTRAN is being 
employed. 
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 1982). Subseque~ly~ 
GM purchased an English-Spanish version of SYSTRAN, 
and began to build the necessary \[very large\] dictionary. 
Sereda (1982) 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 approxi- 
mately $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 result- 
ing documents into French, Italian, Spanish, German, 
and Portuguese. Ruffino (1982) 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-Italian 
(Wheeler 1983). Given this relative success in the CEC 
environment, 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 mili- 
tary equipment into Vietnamese. Due to the termination 
of U.S. involvement in that war, its use was ended after 
two years. (A report by Sinaiko and Klare (1973) 
disparaged LOGOS's cost-effectiveness, but this claim 
was argued to be seriously flawed and was formally 
protested (Scott, personal communication).) The linguis- 
tic foundations of LOGOS are not well advertised, 
presumably for reasons involving trade secrecy. The 
system developer states that "our linguistic approach ... 
has evolved in ways analogous to case grammar/valency 
theory . . . mapping natural language into a semanto- 
syntactic abstraction language organized as a tree" 
(Scott, personal communication). 
LOGOS continued to attract customers. In 1978, 
Siemens AG began funding the development of a LOGOS 
German-English system for telecommunications manuals. 
After three years LOGOS 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 
ff much-reduced demand for translation, hence no imme- 
Computational Linguistics, Volume 11, Number 1, January-March 1985 7 
Jonathan Slocum A Survey of Machine Translation 
diate need for an MT system). Eventually LOGOS forged 
an agreement with the Wang computer company that 
allowed the :implementation of the German-English 
system (formerly restricted to large IBM mainframes) on 
Wang office computers. This system reached the 
commercial market, and has been purchased by several 
multi-national organizations (e.g., Nixdorf, Triumph- 
Adler, Hewlett-Packard); development of other language 
pairs (e.g., English-French, English-German) is under- 
way (Scott, personal communication). 
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 communi- 
cations 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 over- 
view. 
TAUM-METEO was probably also the first MT system 
where translators were involved in all phases of the 
design/development/refinement; indeed, a CMC transla- 
tor 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 Xerox SYSTRAN system, which relies on 
restrictions geared to the characteristics of SYSTRAN. 
But METEO is not extensible - though similar systems 
could be built for equally restricted textual domains, if 
they exist. 
One of the more remarkable side effects of the 
METEO installation is that the translator turnover rate 
within the CMC went from 6 months, 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 24,000 words per day, or 8.5 million 
words per year. Of this, it now correctly translates 
90-95%, shuttling the other ("more interesting") 5-10% 
to the human CMC translators. Almost all of these 
"analysis failures" are attributable to communications 
noise (the CMC network garbles some traffic), 
misspellings (METEO does not attempt corrections), or 
words missing from the dictionary, though some failures 
are due to the inability of the system to handle certain 
linguistic constructions. METEO's computational require- 
ments total about 15 CPU minutes per day on a CDC 
7600 (Thouin 1982). By 1981, it appeared that the built- 
in limitations of METEO's theoretical basis had been 
reached, and further improvement was not likely to be 
cost-effective. 
WEIDNER COMMUNICATIONS CORPORATION 
Weidner was established in 1977 by Bruce Weidner, who 
soon hired some MT programmers from the fading BYU 
project. Weidner delivered a production English-French 
system to Mitel in Canada in 1980, and a beta-test Engl- 
ish-Spanish system to the Siemens Corporation (USA) in 
the same year. In 1981 Mitel took delivery on Weidner's 
English-Spanish and English-German systems, and Brav- 
ice (a translation service bureau in Japan) purchased the 
Weidner English-Spanish and Spanish-English systems. 
To date, there are 22 or more installations of the Weid- 
ner 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 or typesetting codes) and produces a format- 
ted translation. This is an important feature to users, 
since almost everyone is interested in producing format- 
ted translations from formatted source texts. 
Given the way this system is tightly integrated with 
modern word-processing technology, it is difficult to 
assess the degree to which the translation component 
itself enhances translator productivity, versus the degree 
to which simple automation of formerly manual (or poor- 
ly automated) processes accounts for the productivity 
gains. The direct translation component itself is not 
particularly sophisticated. For example, analysis is local, 
usually confined to the noun phrase or verb phrase level 
(except for Japanese) - so that context available only at 
higher levels cannot be taken into account. 
Translation is performed in four independent stages: 
homograph disambiguation, idiom search, structural anal- 
ysis, and transfer. These stages do not interact with each 
other, which creates more problems; for example, homo- 
graphs are resolved once and for all very early on, with- 
out any higher-level context (it is not available until 
later) that would make this process much more sensitive. 
As another example, Hundt (1982) comments that 
"idioms are an extremely important part of the trans- 
lation procedure .... machine assisted translation is for 
the most part word replacement .... " Then, "It is not 
worthwhile discussing the various problems of the \[Weid- 
ner\] 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 neverthe- 
less report economic satisfaction with the results. Hundt 
continues, " . . . \[T\]he Weidner system indeed works as 
an aid . . . " and, "800 words an hour as a final figure 
8 Computational Linguistics, Volume 11, Number 1, January-March 1985 
Jonathan Slocum A Survey of Machine Translation 
\[for translation throughput\] 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 environments. 
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 1983, when a 
controlling interest was purchased by a Japanese compa- 
ny: Bravice, one of Weidner's customers, owned by a 
group of Japanese investors. Weidner continues to 
market MT systems, and is presently working to develop 
Japanese MT systems. A commercial Japanese-English 
system has recently been announced by Bravice, and 
work continues on an English-Japanese system. In addi- 
tipn, Weidner has implemented its system on the IBM 
Personal Computer, in order to reduce its dependence on 
the PDP-11 in particular, and on any one machine in 
general. The system is written in FORTRAN, with some 
assembly code support, but there are plans to reimple- 
ment the software in another language to increase its 
flexibility. 
SPANAM 
Following a promising feasibility study, the Pan Ameri- 
can Health Organization in Washington, D.C. decided in 
1975 to undertake work on a machine translation system, 
utilizing some of the same techniques developed for GAT. 
Consultants were hired from nearby Georgetown Univer- 
sity, the home of GAT. The official PAHO languages are 
English, French, Portuguese, and Spanish; Spanish-Engl- 
ish was chosen as the initial language pair, due to the 
belief that "This combination requires fewer parsing stra- 
tegies in order to produce manageable output \[and other 
reasons relating to expending effort on software rather 
than linguistic rules\]" (Vasconcellos 1983). 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, an in-house translation 
service based on SPANAM was created in 1980. Later 
that year, in its first major test, SPANAM reduced 
manpower requirements for a test translation effort by 
45%, resulting in a monetary savings of 61% (Vascon- 
cellos 1983). (Because these SPANAM translation and 
on-line post-editing figures appear to be contrasted 
against the purely manual, hardcopy translation tradition 
at PAHO, the gains from using SPANAM per se may be 
hopelessly confounded with the gains of working on-line; 
thus, it is difficult or impossible to say how much increase 
in productivity is accounted for by SPANAM alone.) 
Since 1980, SPANAM has been used to translate well 
over a million words of text, averaging about 4,000 
words per day per post-editor. 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. 
Concerning the early status of SPANAM, sketchy 
details implied that the linguistic technology underlying it 
was essentially that of GAT; the grammar rules seemed to 
be built into the programs, in the GAT tradition. The 
software technology was updated in that the programs 
are modular. The system is not sophisticated: it adopts 
the direct translation strategy, and settles for local analy- 
sis of phrases and some clauses via a sequence of primi- 
tive, independent processing stages (e.g., homograph 
resolution) - again, in the Georgetown tradition. 
SPANAM is currently used by three PAHO translators in 
their routine work. 
A follow-on project to develop ENGSPAN (for Engl- 
ish-Spanish), underway since 1981, has also delivered a 
production system - this one characterized by a more 
advanced design (e.g., an ATN parser), some features of 
which may find their way into SPANAM. (SPANAM is 
currently "undergoing a major overhaul" (Vasconcellos, 
personal communication).) Four PAHO translators 
already employ ENGSPAN in their daily work. Based on 
the successes of these two systems, development of 
ENGPORT (with Portuguese as the Target Language) has 
begun. In the future, "all translators \[in the Language 
Services bureau of PAHO will be\] expected to use MT at 
least part of the time, and the corresponding duties are 
included in the post descriptions". (Vasconcellos, 
personal communication). 
CULT: CHINESE UNIVERSITY LANGUAGE TRANSLATOR 
CULT is possibly the most successful of the Machine-aid- 
ed 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-inter- 
active process \[or, at least, with a lot of human inter- 
vention\]. 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 inter- 
vention during translation. Although published details 
(Loh 1976, 1978, 1979) 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 interac- 
tive task.) CULT runs on the ICL 1904A computer. 
Beginning in 1975, 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 
Sini6a. 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- or post-editing. 
Computational Linguistics, Volume 11, Number 1, January-March 1985 9 
Jonathan Slocum A Survey of Machine Translation 
It is not cleat' 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 modifi- 
cation 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 untrans- 
latable 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 determiners. 
ALPS: AUTOMATED LANGUAGE PROCESSING SYSTEMS 
ALPS was incorporated by a group of five Brigham 
Young University ITS developers in 1980; this group 
seems to have been composed of linguists interested in 
producing machine aids for human translators (dictionary 
look-up and substitution, etc.) and later grew to include 
virtually all of the major figures from the ITS staff 
(Melby and Tenney, personal communication). Thus the 
new ALPS system is interactive in all respects, and does 
not seriously pretend to perform translation; rather, 
ALPS provides the translator with a set of software tools 
to automate many of the tasks encountered in everyday 
translation experience. ALPS adopted the language pairs 
that the BYU ITS 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. In addition to selling 
MAT systems, ALPS now includes its own translation 
service bureau. 
The new ALPS system is intended to work on any of 
three "levels" - providing capabilities from multilingual 
word processing and dictionary lookup, through word- 
for-word (actually, term-for-term) translation, to highly- 
automated (though human-assisted) sentence-level 
translation; ~the latter mode of operation, judging by 
ALPS demonstrations and the reports of users, is seldom 
if ever employed. The central tool provided by ALPS is 
thus 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 ALPS system for 
in-house use. Other customers include Xerox, Compu- 
terVision, Control Data (in France), IBM (in Italy) and 
Hewlett-Packard (in Mexico). Recently, another shake- 
up at Weidner Communication Corporation (the Provo 
R&D group was disbanded) has allowed ALPS to hire a 
large group of former Weidner workers: ALPS might 
itself be intending to enter the fully-automatic MT arena. 
CURRENT RESEARCH AND DEVELOPMENT 
In addition to the organizations marketing or using exist- 
ing 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 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 its 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 moti- 
vating force behind this project, and intends that the goal 
be achieved through the development and application of 
highly innovative techniques in both computer architec- 
ture and Artificial Intelligence. 
Of the application areas considered as an applications 
candidate by the ICOT scientists and engineers, Machine 
Translation played a prominent role (Moto-oka 1982). 
Among the western Artificial Intelligentsia, the inclusion 
of MT 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 leader- 
ship, however, the inclusion of MT is no accident. 
Foreign language training aside, translation into Japanese 
is still one of the primary means by which Japanese 
researchers acquire information about what their West- 
ern 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 documen- 
tation not be written only in Japanese, and in general 
translation is seen as a way to "diffuse the Japanese 
scientific and technological information to outer world" 
(Nagao, personal communication). The Japanese 
correctly view translation as necessary to their technolog- 
ical survival, but have found it extremely difficult - and 
expensive - to accomplish by human means: the trans- 
lation budgets of Japanese companies, when totalled, are 
estimated to exceed 1 trinion yen, and most of this 
involves the export trade (Philippi 1985). Accordingly, 
the Japanese government and industry have sponsored 
MT research for several decades. There has been no rift 
between AI and MT researchers in Japan, as there has 
been in the West - especially in the U.S. 
10 Computational Linguistics, Volume 11, Number 1, January-March 1985 
Jonathan Slocum A Survey of Machine Translation 
Nomura (1982) numbers the MT R&D groups in Japan 
at more than 18. (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 
pure development.) Japanese progress in MT has not 
come fast: until a few years ago, their hardware technol- 
ogy was inferior; so was their software competence, but 
this situation has been changing rapidly. Another obsta- 
cle 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 1964. There were once two MT projects at Kyoto 
(one for long-term research, one for near-term applica- 
tion). The former project, recently abandoned, was 
working on an English-Japanese translation system based 
on formal semantics (Cresswell's simplified version of 
Montague Grammar (Nishida et al. 1982, 1983)). The 
latter has developed a practical system for translating 
English titles of scientific and technical papers into Japa- 
nese (Nagao 1980, 1982), and is working on other appli- 
cations of English-Japanese (Tsujii 1982) as well as 
Japanese-English (Nagao 1981). This effort, funded by 
the Agency of Science and Technology and headed by 
Prof. Nagao, "consists of more than 20 people \[at 
Kyoto\], with three other organizations involved \[compris- 
ing another 20 workers\]" (Nagao personal communi- 
cation). The goal of this four-year, $2.7 million (U.S.) 
project is to create a practical system for translating tech- 
nical and scientific documents from Japanese into 
English and vice versa (Philippi 1985). Kyushu Universi- 
ty has been the home of MT research since 1955, with 
projects by Tamachi and Shudo (1974). The University 
of Osaka Prefecture and Fukuoka University also host 
MT projects. 
However, most Japanese MT research (like other 
research) is performed in the industrial laboratories. 
Fujitsu (Sawai et al. 1982), Hitachi, Toshiba (Amano 
1982), and NEC (Muraki & Ichiyama 1982), among 
others, support large projects generally concentrating on 
the translation of computer manuals. Nippon Telegraph 
and Telephone is working on a system to translate scien- 
tific and technical articles from Japanese into English and 
vice versa (Nomura et al. 1982), and is looking into the 
future as far as simultaneous machine translation of tele- 
phone conversations (Nomura, personal communication). 
Recently a joint venture by Hitachi and Quick has 
resulted in a English-Japanese system which will be used 
to offer Japanese readers news from Europe and the U.S. 
on the economy, stock market, and commodities; eventu- 
ally, this service will be offered via Quick's on-line 
market information service (AAT 1984). In addition, 
Fujitsu has announced its bi-directional Atlas Japanese- 
English system for translating technical texts; this system 
is now available for lease (AAT 1984). NEC and IBM 
Japan have also recently announced development of 
systems intended for near-term commercial introduction 
(Philippi 1985). 
Japanese industrialists are not confining their attention 
to work at home. Several AI groups in the U.S. (e.g., SRI 
International) have been approached by Japanese 
companies desiring to fund MT R&D projects, and the 
Linguistics Research Center of the University of Texas is 
currently engaged in MT-related research funded by 
Hitachi. More than that, some U.S. MT vendors 
(SYSTRAN and Weidner, at least) have recently sold 
partial interests to Japanese investors, and delivered 
production MT systems. Various Japanese corporations 
(e.g., NTT and Hitachi) and trade groups (e.g., JEIDA 
(Japan Electronic Industry Development Association)) 
have sent teams 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 (Prof. Shudo at Texas, Prof. Tsujii at Grenoble). 
Other representatives have indicated Japan's desire to 
establish close working communications with the CEC's 
EUROTRA project (King and Nagao, personal communi- 
cation). Japan evidences a long-term, growing commit- 
ment to acquire and develop MT technology. The 
Japanese leadership is convinced that success in MT is 
vital to their future. 
METAL 
One of the major MT R&D groups around the world, the 
METAL project at the Linguistics Research Center of the 
University of Texas, has recently delivered a commer- 
cial-grade system. The METAL German-English system 
passed tests in a production-style setting in late 1982, 
mid-1983, and twice in 1984, and the system was then 
installed at the sponsor's site in Germany for further test- 
ing and final development of a translator interface. 
Renamed LITRAS, it was introduced for sale at the 
Hanover Fair in Germany in April 1985. The METAL 
dictionaries are now being expanded for maximum possi- 
ble coverage of selected technical areas, and work on 
other language pairs has begun in earnest. 
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 for, among other 
Computational Linguistics, Volume 11, Number 1, January-March 1985 11 
Jonathan Slocum A Survey of Machine Translation 
things, arbitrary transformations. The English analysis 
component, on tile other hand, employs a modified GPSG 
approach and makes no use of transformations. Analysis 
is completely separated from transfer, and the system is 
multilingual in that a given constituent structure analysis 
can be used for transfer and synthesis into multiple target 
languages. (Translation from German into Chinese and 
Spanish, as well as from English into German, has tran- 
spired on an experimental basis.) 
The transfer component of METAL includes two 
transformation packages, one used by transfer grammar 
rules and the other by transfer dictionary entries; these 
cooperate 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 are most often paired 1-1 with the 
grammar rules used to perform the original analysis, so 
that there is no need to search through a general transfer 
grammar to find applicable rules (potentially allowing 
application of the wrong ones); however, the option of 
employing a more general transfer grammar is available, 
and is in fact used for the translation of clauses. 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 diction- 
ary 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 manage- 
ment (of dictionary entries and grammar rules), rule vali- 
dation (to eliminate most errors in dictionary entries and 
grammar rules), dictionary construction (to enhance 
human efficiency in coding lexical entries), etc. Aside 
from the word-processing front-end (developed by the 
project sponsor), the METAL group has developed a 
complete system, rather than a basic machine translation 
engine that leaves much drudgery for its human 
developers/users. Lehmann et al. (1981), Bennett 
(1982), and Slocum (1983, 1984, 1985) 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 
the GETA translation system (ARIANE-78) is composed 
of three types of 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 synthe- 
sis). (A fourth type exists, but may be viewed as a 
specialized instance of one of the others.) The overall 
translation process is composed of a sequence of stages, 
wherein each stage employs one of these programs. 
Other modules in ARIANE-78 support editing and system 
maintenance functions. 
One of the features of ARIANE-78 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 mini- 
mally necessary for its proper function. Thus, rather 
than supplying the linguist with programming tools capa- 
ble of performing any operation whatever (e.g., the arbi- 
trarily powerful Q-systems of TAUM), ARIANE-78 
supplies at each stage only the minimum capability neces- 
sary to effect the desired linguistic operation, and no 
more. This reduces the likelihood that the linguist will 
become overly ambitious and create unnecessary prob- 
lems, and also enabled the programmers to produce soft- 
ware that runs more rapidly than would be possible with 
a more general scheme. 
A "grammar" in the ROBRA subsystem is actually a 
network of subgrammars; that is, a grammar is a graph 
specifying alternative sequences of applications of the 
subgramrnars and optional choices of which subgrammars 
are to be applied (at all). The top-level grammar is 
therefore a "control graph" over the subgrammars that 
actually effect the linguistic operations - analysis, trans- 
fer, etc. ARIANE-78 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, it 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 soft- 
ware would have to be changed in many various ways. 
Unfortunately, the underlying software is rigid (written in 
low-level languages), making modification extremely 
difficult. As a result, the GETA group has been unable to 
experiment with any radically new computational strate- 
gies. Back-up, for example, is a known problem (Tsujii, 
personal communication): if the GETA system "pursues a 
wrong path" through the control graph of subgrammars, 
it can undo some of its work by backing up past whole 
graphs, discarding the results produced by entire 
subgrammars; but within a subgrammar, there is no 
possibility of backing up and reversing the effects of indi- 
vidual rule applications. Until GETA receives enough 
funding that programmers can be hired to rewrite the 
software in a high-level language (LISP/PROLOG is being 
evaluated), facilitating present and future redesign, the 
12 Computational Linguistics, Volume 11, Number 1, January-March 1985 
Jonathan Slocum A Survey of Machine Translation 
GETA group is "stuck" with the current software - now 
showing clear signs of age, to say nothing of non-trans- 
portability (to other than IBM machines). 
GETA seems not to have been required to produce a 
full-fledged application early on, and the staff was rela- 
tively free to pursue research interests. Unless the GETA 
software basis can be updated, however, it may not long 
remain a viable system. (The GETA staff are actively 
seeking funding for such a project.) Meanwhile, the 
French government has launched a major application 
effort - Projet Nationale -: to commercialize the GETA 
system, in which the implementation language is LISP 
(Peccoud, personal communication). 
SUSY: SAARBR~CKER ~BERSETZUNGSSYSTEM 
The University of the Saar at Saarbrttcken, West Germa- 
ny, hosts one of the larger MT projects in Europe, estab- 
lished in the late 1960s. After the failure of a project 
intended to modify GAT for Russian-Germad trans- 
lation, a new system was designed along somewhat simi- 
lar lines to translate Russian into German after "global" 
sentence analysis into dependency tree structures, using 
the transfer approach. Unlike most other MT projects, 
the Saarbr0cken group was left relatively free to pursue 
research interests, rather than forced to produce applica- 
tions, 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 MT and AI more closely ,than other projects. For 
example, Saarbriicken helped establish the co-operative 
MT group LEIBNIZ (along with Grenoble and others) in 
1974. 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 
end-user applications. 
Developed as a multilingual system involving English, 
French, German, Russian, and Esperanto, work on SUSY 
has tended to concentrate on translation into German 
from Russian and, recently, English. The strongest limit- 
ing 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 linguis- 
tic rules were organized into strictly independent strata 
and, where efficiency seemed to dictate, incorporated 
directly into the software (Maas 1984). As a conse- 
quence, 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, even though it is used (within 
University projects) for the translation of patent 
descriptions and other materials. 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 
multilingual system, in this case intended for all seven 
European Economic Community languages. The justi- 
fication 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 cost: $43,000 per year), which still 
could not keep up with the demands placed on it; techni- 
cal translation costs the EEC $0.20 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, the translation staff 
would have to double for the current demand level 
(unless highly productive machine aids were already in 
place) (Perusse 1983). The high cost of writing 
SYSTRAN 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 
quality 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 repre- 
sentatives of each member country will produce the anal- 
ysis 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 1982). Software development will be almost 
entirely separated from the linguistic rule development; 
indeed, the production software, though designed by the 
EUROTRA members, will be written by whichever 
commercial software house wins the contract in bidding 
competition. Several co-ordinating committees are work- 
ing with the various language and emphasis groups to 
ensure co-operation. 
The theoretical linguistic basis of EUROTRA is not 
novel. The basic structures for representing "meaning" 
are dependency trees, marked with feature-value pairs 
partly at the discretion of the language groups writing the 
grammars (anything a group wants, it can add), and part- 
ly 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 
1981, 1982). This is not to say that no new linguistic 
knowledge is being gained for, aside from the test of 
theory that EUROTRA is about to perform, there is the 
very substantial matter of the background contrastive 
linguistic investigation that has been going on since about 
1978. 
Computational Linguistics, Volume 11, Number 1, January-March 1985 13 
Jonathan S|ocum A Survey of Machine Translation 
In one sense, the software basis of EUROTRA will not 
be novel either. The basic rule interpreter will be "a 
general re-write system with a control language over 
grammars/processes" (King, personal communication). 
As with ARIANE-78, the linguistic rules can 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. 
In another sense, however, the software basis of 
EUROTRA is quite remarkably different from other 
systems that have preceded it. The analysis, transfer, and 
synthesis strategies will not be incorporated into algo- 
rithms that the programmers implement; rather, they will 
be formulated by linguists and represented in a special 
control language (not the rule-writing language, which is 
algorithm-independent). This formulation of the dynam- 
ic control strategies will be compiled into a program that 
will then interpret the "static" rules describing the 
linguistic facts. 
This is a bold step. There are, of course, pitfalls to 
any such action. Aside from the usual risk of unforeseen 
problems, there are two rather obvious unresolved issues. 
First, it remains to be seen whether linguists, trained 
mostly in the static, "descriptive" framework of linguis- 
tics (modern or otherwise), can accommodate themselves 
to the expression of dynamic algorithms - a mode of 
thinking that programmers (including almost all computa- 
tional linguists) are far more adept at. Second, it also 
remains to be seen whether the system can be designed 
sufficiently flexibly to adjust to the wide range of exper- 
imental strategies that is sure to come when the staff is 
given such a large degree of freedom (remembering that 
the software implementation is seen as an essentially 
one-shot process to be performed on'contract basis), 
while at the same time retaining sufficient speed to 
ensure that the computing requirements are affordable. 
Affordability is not merely an issue belonging to the 
eventual production system! On the contrary, it is crit- 
ically important that a development group be able to 
conduct experiments that produce results in a reasonable 
amount of time. After too long a delay, the difference 
becomes one of category rather than degree, and 
progress is substantially - perhaps fatally - impeded. 
The EUROTRA charter requires delivery of a small 
representative prototype system by late 1987, and a 
prototype covering one technical area by late 1988. The 
system must translate among the official languages of all 
member countries that sign a "contract of association"; 
thus, not all seven EEC languages will necessarily be 
represented, but by law at least four languages must be 
represented if the project is to continue. It appears that 
the requisite number of member states have committed to 
join. It will be interesting to see whether this, the most 
ambitious of all MT projects, succeeds; either way, the 
consequences promise to be noteworthy. 
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 exist- 
ence, whether attempted by machine or man. Even the 
abstract notion of "quality" is undefinable, hence immea- 
surable. In its place, we must substitute the notion of 
evaluation of translation according to its purpose, judged 
by the consumer. 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 trans- 
lations almost universally labelled "bad". Most trans- 
lations, 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" versus "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 - usually post-editors - impose a judgement 
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-editing context, a "good" original 
translation is one worth revising - i.e., one 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 and MAT must respect the operational decisions 
about the quality of MT and MAT as judged by the pres- 
ent users. These systems are all, of course, based on old 
technology ("ancient", by the standards of AI research- 
ers); but by the time systems employing today's AI tech- 
nology hit the market, they too will be "antiquated" by 
the research laboratory standards of their time. Such is 
the nature of technology. We 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). 
PRODUCTION SYSTEMS 
Production M(A)T systems are based on old technology; 
some, for example, still (or until very recently did) 
employ punched 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 
14 Computational Linguistics, Volume 11, Number 1, January-March 1985 
Jonathan Slocum A Survey of Machine Translation 
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 enor- 
mous amounts of time and capital developing the neces- 
sary massive dictionaries specialized to their applications. 
Translation time is certainly reduced. Translator frus- 
tration 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 that have 
been around for years must be economically justifiable to 
their users - else, presumably, they would no longer 
exist. 
DEVELOPMENT SYSTEMS 
Systems being developed for near-term introduction 
employ Computational Linguistics (CL) techniques of the 
late 1970s, if not the 1980s. Essentially all are full MT, 
not MAT, systems. As Hutchins (1982) notes, " . . . 
there is now considerable agreement on the basic strate- 
gy, 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 program- 
ming. 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 implementers) reason to hope for 
near-term success in application. Their technology is 
based on some of the latest techniques that appear to be 
workable in immediate 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 that try to compete. (Since the pres- 
ent crop of development systems all seem to be aimed at 
the "information dissemination" application, the current 
production systems aimed at the "information 
acquisition" market may survive for some time.) The 
major hurdle is time: time to write and debug the gram- 
mars (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 commer- 
cial applications (e.g., GETA) or have just recently done 
so (e.g., METAL, under the market name LITRAS). 
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 foreign languages - even if not for translation per se - 
this would not necessarily be a bad situation. But in the 
U.S. very few are so interested, and in Europe CL and AI 
research has not yet reached the level achieved in the 
U.S. Western business and industry are more concerned 
with near-term payoff, and some track development 
systems; very few support MT development directly, and 
none yet support pure MT 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., Germa- 
ny 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 repre- 
sentations - striving to pursue the goal of a true AI 
system, which would presumably include language-inde- 
pendent meaning representations of great depth and 
complexity. Translation here is seen as just one applica- 
tion ofsuch 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. Trans- 
lation 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 
Computational Linguistics, Volume 11, Number 1, January-March 1985 15 
Jonathan Slocum A Survey of Machine Translation 
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 require- 
ment, however, is the existence of research groups inter- 
ested in, and funded for, the study of multiple languages 
and.translatiort among them within the framework of AI 
research. At the present time only Japan, and to a some- 
what 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 noth- 
ing seems capable of doing so outside of dramatic 
productivity increases via computer automation. In the 
EEC, for example, the already overwhelming need for 
technical translation is projected to rise sixfold within 
five years. 
The future promises 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 improve- 
ments and greater speed, and MT system vendors 
respond. The half-million pages of text translated by 
machine in 1984 is but a drop in the bucket of translation 
demand. 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 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 Linguis- 
tics: Japanese, Chinese, Russian, and even German, for 
example, seem more difficult to deal with using existing 
CL techniques - surely in part due to the nearly total 
concentration of CL workers on English, and their conse- 
quent development of tools specifically for English (and, 
accidentally, for English-like languages). Developing 
translation ability will require similar concentration by 
CL workers on other languages; nothing less will suffice. 
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 
many 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 parti- 
cle physics, a translator really does "fully comprehend" 
the content of the article he is translating. One would 
suspect not. (Johnson (1983) 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 
considerable effort teaching techniques for low-level lexi- 
cal 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 prime 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 multilin- 
gual 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. 

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