EXPERT SYSTEMS AND OTHER NEW TECHNIQUES IN MT SYSTEMS 
Christian BOITET - Ren~ GERBER 
Groupe d'Etudes pour la Traduction Automatique 
BP n ° 68 
Universit~ de Grenoble 
38402 Saint-Martin d'H~res 
FRANCE 
ABSTRACT 
Our MT systems integrate many advanced con- 
cepts from the fields of computer science, linguis- 
tics, and AI : specialized languages for linguistic 
programming based on production systems, complete 
linguistic programming environment, multilevel 
representations, organization of the lexicons 
around "lexical units", units of translation of the 
size of several paragraphs, possibility of using 
text-driven heuristic strategies. 
We are now beginning to integrate new techni- 
ques : unified design of an "integrated" lexical 
data-base containing the lexicon in "natural" and 
"coded" form, use of the "static grammars" forma- 
lism as a specification language, addition of 
expert systems equipped with "extralinguistic" or 
"metalinguistic" knowledge, and design of a kind 
of structural metaeditor (driven by a static 
grammar) allowing the interactive construction of 
a document in the same way as syntactic editors 
are used for developing programs. We end the paper 
by mentioning some projects for long-term research. 
INTRODUCTION 
In this paper, we assume some basic knowledge 
of CAT (Computer Aided Translation) terminology 
(MT, M.AHT, HAMT, etc.). The starting point of our 
research towards "better" CAT systems is briefly 
reviewed in I. In II, we present 3 lines of current 
work : improving current second-generation metho- 
dology by incorporating advanced techniques from 
software engineering, moving toward third-genera- 
tion systems by incorporating expert systems, and 
returning to interactive techniques for the 
creation of a document. 
1 - IMPORTANT CONCEPTS FROM EXISTING SYSTEMS 
For lack of space, we only list our major 
points, and refer the reader to (3,4,5,6,15) for 
further details. 
! - Computer science aspects 
i) Use of Specialized Languages for Linguistic 
Programming (SLLP), like ATEF, ROBRA, Q-systems, 
REZO, etc. 
2) Integration in some "user-friendly" envi- 
ronment, controlled by a conversational interface, 
and managing a specialized data-base composed of 
what we call "lln~-~are" (grammars, dictionaries, 
procedures, formats, variables~ and 
corpuses of texts (source, translated, revised, 
plus intermediate results and possibly 
"hors-textes" -- figures, etc.). 
3) Analogy with compiler-compiler systems : 
rough translation is realized by a monolingual 
analysis, followed by a bilingual transfer, and 
then by a monolingual generation (synthesis). 
2 - Linguistic aspects 
I) Only linguistic levels (of morphology, 
syntax, logico-semantics, modality, actualisation, 
...) are used, leading to some implicit understan- 
ding, characteristic of second-generation MT 
systems. 
2) Hence, the extralinguistic levels (of 
expertise and pragmatics) which furnish some 
degree of explicit understanding are beyond the 
limits of second-generation CAT systems. 
3) During analysis of a unit of translation, 
computation of these (linguistic) levels is not 
done sequentially, but in a cooperative way. 
Analysis produces the analog of an "abstract tre@'~ 
namely a multilevel interface structure to repre- 
sent all the computed levels on the same graph 
(a "decorated tree"). 
4) Lexical knowledge is organized around the 
notion of lexical unit (LU), allowing for powerful 
paraphrasing capability. 
5) The texts are segmented into translation 
units of one or more paragraphs. This allows for 
intersentential resolution of anaphora in some 
not too difficult cases. 
3 - AI aspects 
I) During the structural steps, the unit of 
translation is represented by the current "object 
tree", which may encode several competing interpre- 
tations, like the "blackboard" of some AI systems. 
2)This and the SLLPs' control structures 
allow for some heuristic programming : it is 
possible to explicitly describe and process ambi- 
guous situations in the production rules. 
This is in contrast to systems based on combi- 
natorial algorithms which construct each interpre- 
tation independently, even if they represent them 
in a factorized way. 
468 
II - DIRECTIONS OF CURRENT WORK 
I - Linguistic knowledge processing 
The experience gained by the development of a 
Russian-French translation unit of a realistic size 
over the last three years (6) has shown that main- 
taining and upgrading the lingware, even in an 
admittedly limited second generation CAT system, 
requires a good deal of expertise. Techniques are 
now being developed to maintain the linguistic 
knowledge base. Some of them deal with the lexical 
data-base, others with the definition and use of 
specification formalisms ("static grammars") and 
verification tools. 
Lexical knowledge processin~ 
In the long run, dictionaries turn out to be 
the costliest components of CAT systems. Hence, we 
are working towards the reconciliation of "natural" 
and "coded" dictionaries, and towards the construc- 
tion of automated verification and indexing tools. 
Natural dictionaries are usually accessed by 
lemmas (normal forms). Coded dictionaries of CAT 
systems, on the other hand, are accessed by morphs 
or by lexical units. Moreover, the information the 
two types of dictionaries contain is not the same. 
However, it is highly desirable to maintain some 
degree of coherency between the coded dictionaries 
of a CAT system and the natural dictionaries which 
constitute their source, for documentation purposes, 
and also because these computerized natural dictio- 
naries should be made accessible to the revisors. 
Let us briefly present the kind of structure 
proposed by N. Nedobejkine and Ch. Boitet at an 
ATALA meeting in Paris in \]983. The central idea 
here is to start from the structure of modern 
dictionaries, which are accessed by the lemmas, but 
use the notion of lexical unit. Each item may be 
considered as a tree structure. Starting from the 
top, selections of a "local" nature (on the 
syntactico-semantic behavior in a phrase or in a 
sentence) give access to the "constructions". Then, 
more "global" constraints lead to "word senses". 
At each node, codes of one or more formalized 
models may be grafted on. Hence, it is in principle 
possible to index directly in this structure, and 
then to design programs to construct the coded 
dictionaries in the formats expected by the various 
SLLP. Up to this level, the information is monolin- 
gual and'usable for analysis as well as for genera- 
tion. If the considered language is source in one 
or more language pairs, each word sense may be 
further refined, for each target language, and lead 
to equivalents expressed as constructions of the 
target language, with all other information contai- 
ned in the dictionary constructed in a similar way 
for the target language. For lack of space, we 
cannot include examples. 
This part of the work thus aims at finding 
a good way of representing lexical knowledge 
But there is another problem, perhaps even more 
important. Because of the cost of building machine 
dictionaries, we need some way to transform and 
transport lexical knowledge from one CAT system to 
another. This is obviously a problem of translation. 
Hence, we consider this type of "integrated struc- 
ture" as a possible lexical interface structure. 
Research has recently begun on the possibility of 
using classical or advanced data base systems to 
store this lexical knowledge and to implement the 
various tools required for addition and verifica- 
tion. VlSULEX and ATLAS (1) are first versions of 
such tools. 
Gran~atical knowledge processing 
Just as in current software engineering, we 
have long felt the need for some level of "static" 
(algebraic) specification of the functions to be 
realized by algorithms expressed in procedural 
programming languages. In the case of CAT systems, 
there is no a priori correct gran~,ar of the 
language, and natural language is inherently ambi- 
guous. Hence, any usable specification must specify 
a relation (not a function) between strings and 
trees~ or trees and trees : many trees may corres- 
pond to one string, and, conversely, many strings 
may correspond to one tree. 
Working with B. Vauquois in this direction, 
S. Chappuy has developed a formalism of static 
~rammars (7), presented in charts expressing the 
relation between strings of terminal elements 
(usually decorations expressing the result of some 
morphological analysis) and multilevel structural 
descriptors. This formalism is currently being 
used for all new linguistic developments at GETA. 
Of course, this is not a completely new idea. For 
example, M. Kay (|3) proposed the formalism of 
unification grammars for quite the same purpose. 
But his formalism is more algebraic and less 
geometric in nature, and we prefer to use a speci- 
fication in terms of the kind of structures we are 
accustomed to manipulating. 
2 - Grafting o n expert systems 
Seeing that linguistic expertise is already 
quite well represented and handled in current 
("closed") systems, we are orienting our research 
towards the possibility of addin~ extralinguistic 
knowledge (knowledge about some technical or scien- 
tific field, for instance) to existing CAT systems. 
Also, because current systems are based on trans- 
ducers rather than on analyzers, it is perfectly 
possible that the result of analysis or of transfer 
(the "structural descriptors") are partially 
incorrect and need correction. Knowledge about the 
types of errors made by linguistic systems may be 
called metalinsuistic. 
In his recent thesis (9), R. Gerber has 
attempted to design such a system, and to propose 
an initial implementation. The expertise to be 
incorporated in this system includes linguistic, 
metalinguistic, and extralinguistic knowledge. The 
system is constructed by combining a "closed" 
system, based only on linguistic knowledge (a ling- 
ware written in ARIANE-78), and two "open" 
systems, called "expert corrector systems". The 
first is inserted at the junction between analysis 
and transfer, and the second between transfer and 
generation. 
469 
The control structure of a corrector system 
is as follows : 
(1) transform the result of analysis into a 
suitable form ; 
(2) while there is some error configuration do 
solve (using meta- or extralinguistie 
knowledge) ; 
if solving has failed then exit endif ; 
(4) perform a partial reconstruction of the 
structure, according to the solution found ; 
endwhile ; 
(5) output the final structure in ARIANE-78 format. 
(2) relies on metalinguistic knowledge only. 
The implementation has been done in FolI-PROLOG 
(8). The lingware used corresponds to a small 
English-French system developed for teaching pur- 
poses. Here are some examples. 
Example I : ADJ + N N 
(1) Standard free-energy change is calculated by 
this equation. 
The analyzer proposes that "standard"modifies 
"change", while "free-energy" is juxtaposed to 
"change", hence the erroneous translation : 
"La variable standard d'~nergie libre est calcul~e 
par cette formule". 
In order to correct the structure, some 
knowledge of chemistry is required, namely that 
"standard free-energy change" is a ... standard 
notion. With this grouping, (1) translates as : 
"La variation d'finergie libre standard est calcul~e 
par cette formule". 
Example 2 : (ADJ) N and N N 
(2) The mixture gives off dangerous cyanide and 
chlorine fumes. 
(2') The experiment requires carbon and nitrogen 
tetraoxyde. 
Let us develop this example a little more. 
Sentence (2) presents the problem of determining 
the scope of the coordination. The result of ana- 
lysis (tree n ° 2) groups "dangerous cyanide" and 
chlorine fumes", "chlorine" being juxtaposed to 
"fumes" (SF(JUXT) on node 12). Hence the 
translation : 
"La preparation d~gage le cyanure et la vapeur de 
chlore dangereux". 
But, if we know that cyanide is dangerous as 
fumes, and not as crystals, we can correct the 
structure by grouping "(cyanide and chlorine) 
fumes" (see subtree n ° 2). The translation 
produced will then be : 
"La preparation d~gage la vapeur dangereuse de 
cyanure et de chlore". 
Of course, some more sophisticated analyzers 
would (and some actually do) use the semantic mar- 
ker "chemical element" present on both "chlorine" 
and "cyanide", and then group them on the basis of 
the " semantlc density" (e.g., number of features 
shared). But this technique will fail on (2'), 
because there is no "carbon tetraoxyde" in normal 
chemistry ! Hence, without extralinguistic 
knowledge, this more sophisticated (linguistic) 
strategy will produce : 
"L'expfirience demande du t~traoxyde de carbone et 
d'azote". 
instead of : 
"L'expfirience demande du carbone et du tfitraoxyde 
d'azote". 
RESULTAT DE L'EXECUTION. TEXTE: REHEC PHRASE2 ANALYSE STRUCTURALE 
ULTXT ...... 
I 
I I ' Tree n" 2 ULFRA ...... 2 
I 
IVCL 
...... 3 
I I I 
I~NP s~ 
...... 4 ...... 7 
THE MIXTURE GIVE ...... 5 ...... 6 ...... 8 
I I 
~p 
...... 9 .17 
I I I I 
XAP CYANIDE 
..... IO ..... 12 ..... 13 II 
OANCERO AND QILORIN FUMES 
U .... 11 ..... 14 £ .... 15 ..... 16 
SO~ET 9 ' ': ~('~NP'),RL(ARGI),K(NP),SF(OBJI),~T(N),SUBN(CN), N~(SIN),$~(CONC),SEHCO(SUBST),~I(N). 
SO~ET lO' ': UL('~P'),RS(QUAL),K(AP).SF(ATG),~T(A)tSU~(~J), 
\[MPERS(I~ED),SUBJR(INF). 
S~T II 'DANGEROUS': UL('DANGEROUS'),SF(GOV),CAT(A),SUBA(ADJ), 
SUBJR(INF). 
SOt4HET 12 '~ANIDE': ~'CYANIDE').SFtGOV),~T(N),SUBN(CN),N~(SIH). 
S~(CONC) ,SENCO(S~ST). 
SO~ET 13 ' ': UL('~NP'),RL(ID),K(NP),SF(COO~),~T(N),SUBN(CN). 
N~(PLU),SHM(CONC),SEMCO(SUBST),VLI(N). 
SO~ET 14 'Am': ~('AND'),CAT(C). SOM=MET \]5 'CHLORINE': UL('CHLORINE'),RS(QUAL),UNSAFE(RS),SF(JUXT), 
CAT(N),SUBN(CN),NUH(SIN).SEH(CONC),SEMCO(SUBST). 
SOMHET 16 'F~ES' :~('F~ES' ) ,SF(GOV) ,CAT(N) ,SUBN(CN) ,N~(PLU), SEM(CONC),SEMCO(SUSST). 
TEXTS REHEG PHRASE2 
Analyse structuraIe colfr~.g61 
~P 
i i ...... 9 I 
I 
SAP 
..... IO 
I 
DANGHRO CYANIDE 
U .... II ..... 12 
I I 
FUMES 
..... 9' ..... 16 
I I 
~nP 
..... 13 
AND CHLORINE 
..... 14 ...... 15 
Example 3 : Antecedent of "which" 
(3) The water in the beaker with which the chlorine 
combines will the poisonous. 
The analyzer takes "beaker" instead of"water" 
as antecedent of "which". The corrector may know 
that chlorine combines with water, and not with a 
beaker. 
Examples 4 & 5 : Antecedent of "it" within or 
beyond the same sentence 
(4) The state in which a substance is depends on 
the energy that it contains. When a substance is 
heated the energy of the substance is increased. 
(5) The particles vibrate more vigorously, and it 
becomes a liquid. (5') It melts. 
470 
In order to choose between "substance" and 
"state" (4), one must make some type of complex 
reasoning using detailed knowledge of physics -- 
and one may easily fail in a given context : it is 
not correct to simply state (as we did to solve 
this particular case), that a substance may possess 
energy, while a state cannot. Here, perhaps it is 
better to rely on some (metalinguistic) information 
on the typology, which may be included in a (spe- 
cialized) linguistic analyzer, or in the expert cor- 
rector system. For (5), there are simple, but 
powerful rules like : if the antecedent cannot be 
found in the sentence, look for the nearest 
possible main clause subject to the left. 
3 - Aiding the creation of the source documents 
Lingware engineering may be compared with 
modern software engineering, because it requires 
the design and implementation of complete program- 
ming systems, uses specification tools, and leads 
to research in automatic program generation. Star- 
ting from this analogy, a group of researchers at 
GETA have recently embarked on a project which 
could converge with still another line of software 
engineering, in a very interesting way. The final 
aim is to design and implement a syntactic~semantic 
structural metaeditor that uses a static grammar 
given as parameter in order to guide an author who 
is writing a document, in much the same manner as 
metaeditors like MENTOR are used for writing pro- 
grams in classical programming languages. 
This could offer an attractive alternative to 
interactive CAT systems like ITS, which require a 
specialist to assist the system during the transla- 
tion process. As a matter of fact, this principle 
i~ a sophisticated variant of the "controlled 
syntax" idea, like that implemented in the TITUS 
system. Its essential advantage is to guarantee the 
correctness of the intermediate structure, without 
the need for a large domain-specific knowledge base. 
It may be added that, in many cases, the documents 
being written are in effect contributing some new 
knowledge to the domain of discourse, which hen-c~ce 
cannot already be present in the computerized 
knowledge base, even if one exists. 
III - CONCLUSION : SOME LONG TERM PERSPECTIVES 
There are many areas open for future research 
The introduction of "static grammars" suggests a 
new kind of design, where the "dynamic grammars" 
would be generated from the specifications and from 
some strategies, possibly expressed as "met~-uules". 
"Multisliced decorated trees" (16) have been 
introduced as a data structure for the explicit 
factorization of decorated trees. However, there 
remains to develop a full implementation of the 
associated parallel rewriting rule system, STAR- 
PALE, and to test its linguistic practicability. 
Last but not least, the development of true 
"translation expert systems" requires an intensive 
(psycholinguistic) study of the expertise used by 
human translators and revisors. 
REFERENCES 
(I) Bachut D. - V~rast~gui N. "Software tools for 
the environment of a computer aided translation 
system". COLING-84. 
(2) Barr A. - Feigenbaum E., eds. "The Handbook of 
Artificial Intelligence (vol \],2). Pitman, \]981. 
(3) Boitet Ch. "Research and development on MT and 
related techniques at Grenoble University 
(GETA)". Tutorial on MT, Lugano, April \]984, 
17 p. 
(4) Boitet Ch. - Guillaume P. - Qu~zel-Ambrunaz M. 
"Implementation and conversational environment 
of ARIANE 78.4, an integrated system for trans- 
lation and human revision". Proc. of COLING-82, 
Prag, July 1982, North-Holland, 19-27. 
(5) Boitet Ch. - N~dobejkine N. "Recent develop- 
ments in Russian-French Machine Translation at 
Grenoble. Linguistics \]9, 199-271, 198\]. 
(6) Boitet Ch. - N~dobejkine N. "Illustration sur 
le d~veloppement d'un atelier de traduction 
automatis~e". Colloque "L'informatique au ser- 
vice de la linguistique", Universit~ de Metz, 
juin 1983. 
(7) Chappuy S. "Formalisation de la description 
des niveaux d'interpr~tation des langues natu- 
relies". Etude men~e en vue de l'analyse et de 
la g~n~ration au moyen de transducteurs. Th~se 
de 3~me cycle, USMG, Grenoble, juillet 1983. 
(8) Donz Ph. "Foil, une extension au langage 
PROLOG". Document CRISS, Grenoble, Universit~ 
II, f~vrier \]983. 
(9) Gerber R. "Etude des possibilit~s de coopera- 
tion entre un syst~me fond~ sur des techniques 
de comprehension implicite (syst~me logico- 
s~mantique) et un syst~me fond~ sur des techni- 
ques de comprehension explicite (syst~me ex- 
pert). Th~se de 3~me cycle, Grenoble, USMG, 
janvier \]984. 
(\]O) Hayes-Roth F. - Waterman D.A. - Lenat D.B. eds. 
"Building expert systems". Reading MA, London 
Addison-Wesley, \]983. 
(l\]) Hobbs J.R. "Coherence and co-reference". 
Cognitive sciences 3, 67-90, \]979. 
(\]2) Isabelle P. "Perspectives d'avenir du groupe 
TAUM et du syst~me TAUM-AVIATION". TAUM, 
Universit~ de Montreal, mai 1981. 
(13) Kay M. 
"Unification grammars". Doc. Xerox, 1982. 
(14) Lauri~re J.L. "Representation et utilisation 
des connaissances". TSI \](\],2), 1982. 
(15) Vauquois B. "La traduction automatique 
Grenoble". Document de Linguistique Quantita- 
tive n ° 29, Dunod, 1975. 
(16) V~rast~gui N. "Etude du parall~lisme appliqu~ 
la traduction automatis~e par ordinateur. 
STAR-PALE : un syst~me parall~le". Th~se de 
Docteur-lng~nieur, USMG & INPG, Grenoble, 
mai 1982. 
471 
