ITS: INTERACTIVE TRANSLATION SYSTEM 
Alan K. Melby, Melvin R. Smith, Jill Peterson 
Brigham Young University 
Provo, Utah U.S.A. 
At COLING78 we reported on an interactive 
translation system now called ITS, which uses 
on-line man-machine interaction. This paper 
is an update on ITS with suggestions for future 
work. 
Summary of ITS 
ITS is a second-generation machine trans- 
lation system. Processing is divided into 
three major steps: analysis, transfer, and 
synthesis. Analysis is generally independent 
of the target language, and synthesis is 
nearly independent of the source language. 
The transfer step is dependent on both source 
and target languages. The intermediate 
representation produced by analysis, adjusted 
by transfer, and processed by synthesis is 
defined by Junction Grammar I and consists of 
objects called J-trees. 
The general characteristics of ITS, 
namely, modular design and a full syntactic 
analysis as an intermediate representation, 
place ITS in the family of second generation 
systems including the Montreal project and the 
Grenoble project. 
However, there are some further character- 
istics of ITS which must be mentioned to allow 
a better understanding of the system. 
The source texts for ITS are selected from 
rather general English found in the publica- 
tions of the LDS Church. These publications 
are not at all restricted to theological 
dissertations. They include articles from 
many authors in many styles on subjects ranging 
from gardening to short stories to descriptions 
of foreign cultures to parent-child relations. 
The source texts for the Montreal project, 
on the other hand, consist of documents from a 
carefully selected sub-language. Their first 
commercial system, the TAUM-METEO system, 
translates weather forecasts. Their current 
project, TAUM-AVIATION, is much more ambitious 
but is nevertheless restricted to manuals con- 
cerning the hydraulic systems of jet aircraft. 
When a system is designed to translate a 
particular sub-language, the system can take 
advantage of the properties of that sub- 
language. 
Returning to the general English texts 
which are the specified input to ITS, we find 
that there are no well-defined properties of 
the texts which can be used to reduce ambi- 
guity. This is why it was decided to use 
on-line man-machine interaction during the 
actual process of translation in ITS. This 
human interaction is, of course, expensive and 
so it was decided to share the burden of inter- 
action by analyzing the source text once and 
using the result of analysis as the input to 
several transfer-synthesis pairs. This means 
that ITS is a one-to-many system. Being 
one-to-many not only allows the overhead of 
analysis to be shared but also provides an 
element of consistency in the output across 
languages. This is because all the target 
language texts are derived from the same 
intermediate representation produced by 
analysis. 
Thus, ITS is a second generation, 
interactive, one-to-many translation system 
intended for general English text. The 
current target languages are Spanish, 
Portuguese, German, French, and (on a limited 
basis) Chinese. 
Status of ITS 
During the last two years, ITS has been 
developed to the point where it has been 
tested for the possibility of commercial use. 
The dictionaries contain nearly thirty thousand 
words, not counting idioms and other multi-word 
units. The system has recently translated a 
series of test passages totaling over ten 
thousand words. 
The results of the tests were encouraging. 
The system was shown to be capable of trans- 
lating rather general text. Thanks to human 
interaction and an extensive English grammar, 
nearly all the sentences of the source text 
receive a full Junction Grammar analysis 
which contains some semantic information beyond 
phrase structure; and the output, though far 
from perfect, is worth post-editing, and could 
be considerably improved with more dictionary 
work. 
A major disappointment was the amount of 
time spent on human interaction. It was 
originally hoped that interaction could be 
restricted to analysis, since the overhead of 
analysis is shared by several target languages. 
However, it was found that some interaction was 
also required in transfer. The problem is that 
interaction in transfer is specific to one 
target language and is not shared. The result 
is that the average amount of interaction per 
page (~50 words) of text is currently about 
seven minutes for analysis (i.e. about thirty- 
five minutes shared by five languages), ten 
minutes for transfer interaction and fifteen 
minutes for post-editing. That makes a total 
of about thirty minutes per page per language. 
These thirty minutes per page include a post- 
edit step by a human translator and so the 
~424 
translation is roughly equivalent to a first 
draft translation by a human translator. The 
time spent per page by a human translator 
varies considerably but averages to approxi- 
mately one hour per page. 
Thus ITS appears to be somewhat faster 
than human translation and promises a degree 
of consistency when a large document involves 
several translators. However, an examination 
of the total process of translation by manual 
methods and by the current version of ITS 
resulted in a recent decision not to attempt a 
commercialization of ITS at this time. Some 
points to consider are the following: 
(I) Even though ITS may be slightly faster 
than manual translation, it is not yet fast 
enough to justify the effort required to build 
and maintain the dictionaries and the expense 
of maintaining a computer to run ITS on. 
(2) The on-line interaction requires specially 
trained operators. 
(3) Most translators do not enjoy post-editing 
machine translation. 
(4) The scheduling of processing in a one-to- 
many system is rather tedious. 
Even though the current version of ITS is 
not being commercialized, the authors remain 
optimistic about the future of interactive 
machine translation. 
Interactive translation will be the most 
useful in translating texts which are too 
general or varied to be considered part of a 
sub-language. The limits of fully-automatic 
translation are well-known. To date, fully 
automatic translation has been shown to be 
commercially useful only when it is intended 
to be merely indicative (e.g. Russian-English 
MT at Rome Air Force Base) or when the system 
is tailored to a sub-language (e.g. TAUM- 
METEO). If the need is for high-quality 
translation of general text, the only possi- 
bilities seem to be (i) a highly successful 
large-scale AI approach, probably with a 
self-learning capability or (2) an interactive 
approach, with limited self-learning capability 
if possible. 
To be more specific on option (2), the 
authors foresee a translator's work station 
which would support two modes of operation. 
In one mode it would be a sophisticated but 
easy to use word processor. Of course, 
multiple character sets would be available on 
the video display and the typewriter quality 
hardcopy device. The station would also con- 
tain a glossary which could be updated by the 
translator. The translator could even link up 
to a major terminology bank over the phone. 
Martin Kay (Xerox) is currently working on 
such a station, with his own variations. 
In the second mode, the work station 
would be an interactive translation system. 
Source text could be entered directly from the 
keyboard or the translator could insert a 
diskette containing the source text as it was 
first entered on a word processor for publica- 
tion in the source language. Eventually, OCR 
may be a practical input process for such a 
station. At any rate, after the source text 
is entered, the station would interactively 
resolve ambiguities and other problems in the 
translation. The interaction, to be attrac- 
tive, would have to average under ten minutes 
per page for a one-to-one translation and the 
output would have to be of such high quality 
that it could pass as a human translation with 
only a few minor post-edit changes per page. 
The work station would also have to be 
reasonably priced (less than a compact auto- 
mobile). 
Finally, and most importantly, the work 
station must work! That is, the station must 
be easy enough to use that the translator will 
want to use it. The first mode (word 
processor with dictionary lookup) must allow 
the translator to produce a quality translation 
faster than by manual methods. And the second 
mode (interactive translation system) must 
allow the translator to produce a quality 
translation faster than by using a word 
processor. 
When a work station such as the one just 
described comes about, there may be few 
translators who want to try it, even if it 
works. Of course, if it does work, translators 
will have to have been involved in its develop- 
ment. But once a few translators venture 
voluntarily to use it and find that it makes 
them more productive and cost effective, then 
the pressure to use it will come from within 
the translator community, not from outside. 
This is one view of how the computer will 
be used in translation. Rather than replacing 
human translators, computers will serve human 
translators. It agrees with Andreyewsky's 
advice to translators: "instead of fighting a 
'win/lose' battle with the machines, we must 
work toward developing an 'everybody wins' 
frame of reference. ''2 
The rest of this paper will consist of 
several independent comments on some of the 
work on ITS the last two years which may be 
useful or at least interesting to others 
working in machine translation. The comments 
will concern word selection problems, inter- 
action, and linguistic programming languages 
used in transfer. 
.... 425- 
Word Selection 
Selection of an appropriate translation, 
that is, replacement of source words and 
phrases is much more challenging in general 
text than in a sub-language. Several years 
ago, it was hoped that interaction in analysis 
to select word senses would permit selection 
of translation equivalents in transfer without 
further interaction. Instead it was found 
that the mapping between words in English and 
multiple target languages is so variable that 
no one set of English word senses will 
satisfy all the target languages. To illus- 
trate this, two sample words will be considered: 
"run" and "line." Sample phrases taken from 
a corpus of LDS English will be given. A 
variety of uses on a single word is manageable 
but consider the problem of anticipating all 
possible translations and how to distinguish 
them on more than thirty thousand words. 
Run 
"their provisions might run short" 
"the train continued its run" 
"I tried to run the pleasant thoughts of the 
day through my mind" 
"she ran her fingers through the boy's hair" 
"time had run out" 
"someone opened the door and the dog ran out" 
"sometimes chicken and sheep run in the street" 
"he would wait until called to run an errand" 
"how to run the organization" 
"it ran its course" 
"the bull ran its horns into the pumpkin" 
"seeing the wave come, they ran into the trees" 
"the bitterness ran deep" 
"sometimes meetings ran late" 
"confusion ran rampant" 
"water runs faster at the surface" 
"no running water" 
"he took a running start" 
"shirt and running shorts" 
Line 
"the line of march" 
"line of communication" 
"line of authority" 
"front line of attack" 
"over the end line but not between the goals" 
"the next line or ancestor in question" 
"thirty people stood in line" 
"born of a royal line" 
"the first line of a story" 
"the bottom line (conclusion) is clear" 
"draw a crazy (crooked) line on a piece of 
paper" 
"they would be taught line upon line" 
"the hard leathery lines of grandmother's face" 
"those production lines" 
Interaction 
The use of interaction is not a panacea. 
It is difficult to know when to ask a question. 
A major reason for the excessive interaction 
in the current version of ITS is the problem of 
knowing when to ask a particular question. How 
does one know if a particular ambiguity is 
going to make a difference in the translation 
or if the ambiguity will be preserved? Another 
problem is that many sentences that are not 
ambiguous to a human contain ambiguities which 
a machine can resolve automatically only by 
semantic tests. Semantic tests can be made in 
sub-languages with some success, but it is not 
easy. In general text, the increased variety 
means that semantic tests are much more 
difficult to make reliably. Consider, for 
example, the problem of making a general sys- 
tem that would naturally take care of the 
following ambiguities: 
(i) The fact that the man knew surprised us. 
(the fact which he knew vs. the fact of 
his knowing) 
(2) I saw her shaking hands. 
(...her hands were shaking vs. she shook 
hands with someone) 
(3) I made my brother a statue. 
(I made a statue for him vs. I made him 
into a statue) 
(4) Beware of falling victims to error. 
(beware of becoming victims or beware of 
victims that are falling, cf "beware of 
falling rocks") 
The last example is not ambiguous to a human 
but how does one resolve the ambiguity without 
a rule specific to "falling?" 
Hopefully, AI techniques will someday be 
able to resolve such ambiguities in large scale 
systems as well as they now do in restricted 
tests systems. Indeed, major advances in AI 
may well be essential to acceptable functioning 
of the work station described above, but there 
is enough of Joseph Weizenbaum in me to believe 
that even in the long run human interaction 
will be needed to produce high quality raw out- 
put in the machine translation of general text. 
A reasonable question would be: "why not 
guess on the unsure points during translation 
and let the post-editor clean them up?" One 
answer is that raw output must be close to 
final form or the post-editor will either want 
to start over or will not post-edit up to high 
quality. In other words, a post-editor will 
only improve a text by a certain increment. 
426 
This has been determined in experience with 
manual human translation being post-edited by 
another human. Another answer is that inter- 
action may well point out ambiguities which 
could be missed by a human translator-- 
particularly a native of the target language. 
An empirical method of determining the 
appropriate mix between interaction and post- 
editing is to have the program attach to 
each potential interaction a guess with a con- 
fidence level. Then the translator sets an 
interaction level. Then interactions will 
not occur if the confidence level exceeds the 
interaction level. This technique has been 
partially implemented in ITS and seems informa- 
tive. 
Transfer Languases 
For those interested in comparing systems, 
we include two sample entries from the transfer 
dictionary of ITS 3 and two sample entries from 
the transfer dictionary of the TAUM-AVIATION 
system. 4 
adjective, set the preposition, and make the 
direct object the object of the preposition. 
J-TREE BEFORE: 
/½ 
V + N 
adjoin the 
building 
J-TREE AFTER: 
v 
ser 
conti/guo 
com the 
building 
ITS i ITS 2 
ADJOIN 
1 &s map 
2 ser conti/guo com \[l.v 
3 unir .v 
4 &w("l") c =pa;ser;conti/guo;com 
=PA 
1 &s p verb;pa;prep 
2. /* verb becomes copula with pred adj 
modified by PP */ 
3 &s *,f (,ad) verb;pa 
4 &s m (,ad) 
5 &s f,do (do 
6 &t *,t (ad,,,do) ;prep 
DESCRIPTION: 
(ADJOIN) 
1-3: Interact on the two translation choices 
given. 
4: When "i" is returned from the MAP action 
code ("ser conti/guo eom" was chosen), call 
dictionary subroutine = PA, passing the 3 
parameters shown. 
(=PA) 
i: Associate the 3 names given with the para- 
meters passed. 
2: Comment 
3: Build a predicate adjective, setting the 
verb and predicate adjective to the values 
("ser" and "conti/guo") of the respective 
parameters. 
4: Move any insertions on the verb to the 
predicate adjective. 
5: Find the direct object of the verb (if 
any). 
6. If direct object is found then build a 
prepositional phrase modifying the predicate 
AFTER 
1 &s f,do (,do 
2 &t &do/* preposition */ 
3 &w(f,*,f,sxt,v, (do)) s depois/* obj is 
full SV */ 
4 &w(f,*,f,v (dO)) s depois de/* obj is PV */ 
5 &o map/* interact */ 
6 depois de .w~time> 
7 atra/s de .w<place> 
8 apo/s .w<iterative> 
9 &end 
i0 &e map/* adverbial particle */ 
ii depois .w<time> 
12 atra/s .w~lace) 
DESCRIPTION: 
I: Find prepositional object. 
2: If found, then execute &do-group. 
3: When object is a noun clause, set "after" 
to "depois". 
4: When object is a gerund, set "after" to 
"depois de". 
5-8: Otherwise, interact on the three trans- 
lation choices given (utilizing disambigu- 
ation strings if present). 
9: End of &do-group. 
10-12: If object was not found, interact on 
the two translation choices given (check 
for disambiguation strings). 
NOTE: This transfer is referential only and 
no structural manipulation is performed. 
-427 
TAUM I 
#FILTER# == 
(*C.LAINE, LE 6 DECEMBER 1978. "FILTERED" 
EMPLOYE AVEC UN NOM DE PIECE DEVRAIT SE 
TRADUIRE PAR "A FILTRE". POUR LE MOMENT NOUS 
ALLONS LE RENDRE PAR "MUNI D'UN FILTRE"*) 
DEBUT 
SI NATURE (FC) EST V ALORS 
DEBUT 
SI PARCOURS/CV/GV GPREP CP P SEPO ALORS 
TRADUIRE EPO PAR #BOF#; 
SI PARCOURS/CV/GV/GOV/PH TELQUE TRAITS 
(ICI) =\[APRENOM\] 
$EPH ALORS 
DEBUT 
TRADUIRE FC PAR #MUNIR# \[Bi9\]: 
A1 := GP(GPREP(CP(P UT TRADUITE #DE#)), 
GN(CN(N UT 
TRADUITE # FILTRE#), DET(CART(ART UT 
TRADUITE #UN#)))): 
DEPLACER A1 EN OBJI\[i\] SOUS EPH 
FIN 
SINON 
DEBUT 
TRADUIRE FC PAR #FILTRER#\[B6\]; 
TRAITS EN FAC := TRAITS (FC)+\[TNOREF\] 
FIN 
FIN 
SINON 
TAUM 2 
#PRESENT#== 
(* S.L.,8.01.79. TEST: SI ADJ EST PRENONIMAL 
TRADUIRE PAR "PRESENT", SINON TRADUIRE "S BE 
PRESENT" PAR "IL Y AVOIR X". CETTE 
RESTRUCTURATION EST COMPLEXE. CF. LE CAHIER 
DES ADJECTIFS POUR UNE REPRESENTATION DE LA 
RESTRUCTURATION. *) 
VAR Ai:UT; A2:ARBRELIBRE; A3:ARBRELIBRE FIN 
DEBUT 
SI NATURE (FC) EST ADJ ALORS 
DEBUT 
SI PARCOURSE /CADJ /GV /GOV /PH TELQUE 
TRAITS(ICI) =\[AREL\] /GN ALORS 
(* CE TEST SERT A SAVOIR SI L'ADJECTIF EST EN 
POSITION PRENOMINALE. *) 
DEBUT 
SI PARCOURS /CADJ $ECADJ /CONJ GV $EGV 
/CONJ GOV (OPS CONJ (COP 
TELQUE (UT(ICI) = 
#BOF#) OU (UT(ICI)=#BE#) 
$ECOP)) $EGOV 
/CONJ PH $EPH ALORS 
(* ON NE TESTE PAS SI PH EST CONJOINT, CELA 
N'EST PAS PERTINENT; ON NE TESTE PAS SI GV 
DOMINE DES MODIFIEURS, PUISQUE CEUX-CI SONT 
LES MEMES POUR LES ADJECTIFS ET LES VERBES.*) 
DEBUT 
Ai:=#V# #AVOIR#\[Bi\] ; 
A2:=CV (V UT TRADUITE AI) : 
REMONTER FC EN EGV ; 
DEPLACER A2 EN CV SOUS EGV ; 
EFFACER ECOP ; 
EFFACER ECADJ ; 
A3:=GP (GPREP (CP(P UT TRADUITE #BOF# ), 
EN SUBS GN(PRON UT TRADUITE #1L#)) ; 
SI PARCOURS EPH SUJ $ESUJ ALORS 
DEBUT 
SI PARCOURS ESUJ _ OBJD( ) $#OBJDALORS 
DEBUT 
INSERER CIRC(EN GP DEPLACE (EOBJD)) 
EN CIRC\[i\] SOUS EPH; 
COPIER ESUJ EN OBJD SOUS EPH ; 
DEPLACER A3 EN SUJ SOUS EPH ; 
FIN 
SINON 
DEBUT 
COPIER ESUJ EN OBJD SOUS EPH ; 
DEPLACER A3 EN SUJ SOUS EPH ; 
FIN 
FIN 
SINON 
DEBUT 
TRADUIRE FC PAR UT(FC) ; 
ECRIRE SOMMET ; 
FIN 
FIN 
SINON 
DEBUT 
TRADUIRE FC PAR UT(FC) ; 
ECRIRE SOMMET ; 
FIN 
FIN 
SINON 
DEBUT 
TRADUIRE FC PAR #PR2ESENT#\[Fi,Pi\] ; 
FIN 
FIN 
SINON 
(*C. LAINE, LE 27 DECEMBRE 1978") 
SI NATURE (FC) EST V ALORS 
DEBUT 
TRADUIRE FC PAR #PR2ESENTER# \[B6\] 
FIN 
SINON 
DEBUT 
TRADUIRE FC PAR UT(FC) ; 
FIN 
FIN. 
(* FIN DE #PRESENT# *) 
428 

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