From HOPE en I'ESPERANCE 
On the Role of Computational Neurolinguistics in Cross-Language Studies I 
Helen M. Gigley 
Department of Computer Science 
University of New Hampshire 
Durham, NH 03824 
ABSTRACT 
Computational neurolinguistics (CN) is an 
approach to computational linguistics which in- 
cludes neurally-motivated constraints in the 
design of models of natural language processing. 
Furthermore, the knowledge representations in- 
cluded in such models must be supported with 
documented behaviorial ev~ce, normal and patho- 
logical. 
This paper will discuss the contribution of 
CN models to ~the understanding of linguistic 
"competence" within recent research efforts to 
adapt HOPE (Gigley 1981; 1982a; 1982b; 1982c; 
1983a), an implemented CN model for "under- 
standing" English to I'ESPERANCE, one which "un- 
derstands" French. 
I. INTRODUCTION 
Computational Neurolinguistics (CN) incorpor- 
ates initial assumptions about language processing 
that are often indirectly referenced in other 
computational approaches to language study. These 
assumptions focus on neural-like computational 
mechanisms (Ballard 1982; Feldman 1981; Gigley, 
1982a; 1982b; 1983a; McClelland and Rumelhart, 
1981) which subserve language behavior (Lavorel 
and Gigley, 1983). 
Furthermore, CN approaches to different 
aspects of language processing include extensive 
use of behavioral data. Research exists within 
the CN paradigm along various behaviorally defined 
dimensions. These are at the level of phonetic 
speech studies that simulate speech errors (Le- 
cours and Lhermitte, 1969; Reggia and Sanjeev, 
1984), a model of aphasic language production, 
JARGONAUT, (Lavorel, 1982), as well as within 
lesionable models at a neural network level. 
These latter models simulate association, dis- 
crimination, and recognition of patterns employing 
associative network models that have been tuned or 
have adaptively learned to relate certain dis- 
criminations (Gordon, 1982; Wood, 1978; 1980). 
IThe research described in this paper was sup- 
ported by an NIH-CNRS research exchange grant 
entitled "Computational Neurolinguistics" and was 
undertaken at Laboratoire de Neuropsychologie 
Exp~rimentale, INSERM-Unit~ 94, BRON, France. 
There is much philosophical and linguistic 
discussion of the nature of the representations 
that exist in humans and form the basis of our 
cognitive function. We will not present the 
debate here, but instead will claim that the CN 
models we build include the assumption that the 
internal representation of concepts, words, and 
phonemes are given by the overall activation state 
of the "network" representation within the system 
at a moment in time. Furthermore, this means that 
unless activations are interpreted externally (in 
our case by labels so that we can talk about 
them), they in and of themselves reflect the 
"mental" representation. 
To this end, CN models present time- 
synchronized snapshots of an interactive, paral- 
lel, distributed process that are interpreted to 
represent hierarchies of linguistic knowledge that 
can be distinguished during processing, such as a 
recognized word, a grammatical interaction, or 
even a disambiguated meaning. 
Before turning to our efforts to adapt a 
working implementation within the CN paradigm, 
HOPE, into one that can process French with equal 
facility, I'ESPERANCE, we will present necessafy 
background to illustrate why focusing on the 
"process" of language, as it can exist, based on 
our current understanding of brain function, 
contributes significantly to our increased under- 
standing of representations which have been de- 
fined within linguistics, psycholinguistics, 
neurolinguistics, and AI approaches to language 
study. 
2. FOCUS ON PROCESS 
In developing CN models, the claim is that by 
focusing on process independently from repre- 
sentation, we gain several perspectives that are 
unattainable from other more usual approaches. CN 
models include processing which is neurally 
plausible. Language is seen as the behavioral 
result of an interactive, time-dependent process. 
This frees us from pre-specifying either all 
"correct" linguistic possibilities for constraint 
satisfaction at all levels of representation, or 
all possible errors or recognized omissions as in 
more flexible approaches (Hayes and Mouradian, 
1981; Kwasny and Sondheimer, 1981; Lehnert, Dyer, 
Johnson, Yong, and Hurley, 1983; Weischeidel and 
Black, 1980). 
452 
We utilize what has been discovered by these 
other approaches to be the most likely, most 
plausible set of relevant features to tune our 
"normal" model. Through interconnections at a 
metalinguistic level, between recognized phonetic 
word representations, grammatical aspects of 
meaning, and specific referential meaning for 
disambiguated words, CN models must tune the 
process so that asynchronously activated in- 
stantiations at these interpretable levels which 
result from local contextual recognition achieve 
the same behavioral results that are defined 
within different methodologies. In other words, 
we use the A! preconditions or ATN states with as 
much corroboration from psychological, and 
linguistic studies as is available to tune our 
models for "normal" processing. 
This provides an extremely valuable means of 
studying processing effects in neurally motivated 
"lesion" states that are consistent within our 
system, and completely defined over our model of 
study in a mathematical sense. This has been 
discussed in detail elsewhere in Gigley (1982b; 
1983a; 1983b), and Gigley and Duffy (1982) and 
will not be repeated here. 
3. PROCESSING ASSUMPTIONS IN HOPE 
HOPE is not an acronym but was chosen as the 
name of the system based on the legend of 
Pandora's box. While raising many questions of 
language within a new computational perspective, 
it provides a first attempt to answer them as 
well. 
The system presents an initial attempt to 
integrate AI and brain theory, BT, on two levels, 
behaviorally and within processing. HOPE uses 
concepts from cellular neurophysiology to define 
its control. Information in HOPE is encoded in a 
hierarchical graph which permits extensive 
ambiquity. 
For complete detail of the model with exam- 
ples in "normal" and "lesioned" states the inter- 
ested reader is referred to Gigley (1982a; 1982b; 
1983a). We will only highlight the processing 
here. 
HOPE stresses the process of natural language 
by incorporating a neurally plausible control that 
is internal to the processing mechanism. There is 
no external evaluation made to decide what happens 
next. At each process time interval, there are 
six types of serial-order process that can occur 
and affect the state of the process. The most 
important aspect of the control is that all of the 
serial order computations can occur simultaneously 
and affect any information that has been defined 
in the model. 
Similar control philosophies have been em- 
ployed in letter perception by McClelland and 
Rumelhart (1981), and in the connectionist 
theories applied to visual processing and language 
parsing (Ballard, 1982; Cottrell, 1983; Feldman, 
1982; Small, Cottrell, and Shastri, 1982). 
The major difference in the control in HOPE 
is that the control process can be "lesioned" by 
modifying parameter settings relative to their 
"normal" settings to define hypothesized causes of 
pathological language behavior. Example "lesions" 
are changes in memory decay, elimination of a 
knowledge type, and slowing of processing relative 
to on-line word recognition. 
Studying the results of such "lesions" and 
their occurrence or not in pathological behavior 
is used to further understanding of the behavior 
and to suggest evolutionary changes in the model 
to better its approximation to language process. 
Information is presented at a phonological 
level as phonetic representations of words, at a 
word m~aning level as multiple pairs of designed 
syntactic category types and orthographic spelling 
associates, within grammar, and as a pragmatic 
interpretation. 
Each piece of information is a thresholding 
device with memory. It has an activity value, 
initially at a resting state, that is modified 
over time depending on the input, interconnections 
to other information, and an automatic activity 
decay scheme. In addition, the decay scheme is 
based on the state of the information, whether it 
has reached threshold and fired or not. 
Activity is propagated in a fixed sense to 
all aspects of the meaning of words that are 
"connected" by spreading activation. (Collins and 
Loftus, 1975; Quillian, 1980/73; Small, Cottrell, 
and Shastri, 1982; Cottrell, 1983). Simultan- 
eously, information interacts asynchronously due 
to threshold firing. This is achieved by the time 
coordination of asynchronously encoded serial 
order processes. The serial-order processes that 
occur at any moment of the process are context 
dependent; they depend on the "current state" of 
the system. 
The serial order processes include: 
I. NEW-WORD-RECOGNITION: Introduction of the 
next phonetically recognized word in the 
sentence. 
2. DECAY: Automatic memory decay reduces the 
activity of all active information that does 
not receive additional input. It is an im- 
portant part of the neural processes which 
occur during memory access. 
3. REFRACTORY-STATE-ACTIVATION: An automatic 
change of state that occurs after active 
information has reached threshold and fired. 
In this state the information can not affect 
or be affected by other information in the 
system. 
4. POST-REFRACTORY-STATE-ACTIVATION: An auto- 
matic change of state which all fired in- 
formation enters after it has existed in the 
REFRACTORY-STATE. The decay rate is differ- 
ent than before firing. 
453 
5. MEANING-PROPAGATION: Fixed-time spreading 
activation to the distributed parts of 
recognized words ' meanings. 
6. FIRING-INFORMATION-PROPAGATION: Asynchronous 
activation propagation that occurs when 
information reaches threshold and fires. It 
can be INHIBITORY and EXCITATORY in its 
effect. INTERPRETATION is a result of acti- 
vation of a pragmatic representation of a 
disambiguated word meaning. 
It is the in interaction of the results of 
these asynchronous processes that the process of 
comprehension is defined. 
The processes are independent of the know- 
ledge representations defined and are blindly 
applied across all of them. This often produces 
unexpected but humanly interpretable results when 
the end state is compared with suitably defined 
behavioral test results. 
During processing, we can study both the 
change in state that results over time and "how" 
the change occurred. Analyzing both aspects of 
the process is the focus of comparison between 
"normal" and "lesion" performance of the model. 
In this way we are able to study the effect of the 
"lesion" in a well defined linguistic context, and 
to make behavioral predictions that can be veri- 
fied (Gigley, 1982b; 1983a; 1983b; Gigley and 
Duffy, 1982). 
4. FROM HOPE en I'ESPERANCE 
Given that CN approaches to natural language 
processing assume a neural-like control paradigm, 
it is possible to assume that such a paradigm will 
work equally well for other natural languages by 
simply recoding the representations into the 
second language surface representation, grammar, 
and semantic structure. We assume that the pro- 
cesses can be tuned to produce "normal" results as 
they have been for the simple English fragment 
demonstrated to date. 
As a first attempt to determine if such a 
cross-linguistic adaptation is possible, we have 
begun to redefine the knowledge representations to 
encode suitable representations of French, homo- 
logous to those that HOPE includes in its present 
level of implementation. 
The beginnings of the adaptation raised 
questions about language representation from a 
different perspective than occurs within a 
strictly linguistic analysis. The remainder of 
the paper focuses on our initial work in the 
adaptation (Gigley, 1984). As the research is 
currently underway, the discussion will raise 
several unanswered questions in pointing out the 
value of applying a CN methodology to cross- 
linguistic study. 
In explaining the representation issues for 
French, we will first, briefly provide background 
in current linguistic research on French. This 
will include an overview of recent relevant 
psycholinguistic and neurolinguistic studies in 
French. Then we will present an overview of 
computational natural language systems for speech 
recognition comprehension and automatic transla- 
tion into French. One issue, how to chunk French 
into a phonetic representation of words, along 
with the implications of the determined repre- 
sentation for our processing approach to compre- 
hension of French, will form the basis of the 
discussion. 
4.1 Word Boundaries in On-Line Comprehension 
of French 
Because of the parallel activation of all 
meanings of each recognized word in HOPE, the 
determination of the phonetic representation of a 
recognized word determines the breadth of active 
competition amon 9 meanings for subsequent time 
intervals of the process. Depending on how the 
words are chunked, different homophone sets, sets 
of associated meanings for a given homophone, may 
arise. 
For spoken English, word boundaries tend to 
be marked by intonation or pauses. However, for 
French this is not the case. Depending on the 
context, the ending of one word may be phone- 
tically affixed to the following one called 
liason. In addition, when a content word begins 
wl~ vowel or silent h, the ending vowel of the 
preceding word is dropped, called elision. 
The problem is particularly evident with 
respect to the use of articles which are very 
often spoken in such context. In addition, these 
same articles do not have the same meaning as they 
do in English. "Le, la, les" do not always mean 
"the" in the definite sense, but are often generic 
and mark masculine, feminine, or plural (Gross, 
1977; Goffic and McBride, 1975). And furthermore, 
these same articles often are not translated into 
meaning preserving sentences in English. An 
example sentence demonstrating this is: Ce singe 
aime le cafe. (This monkey likes coffee.) 
The degradation of these same morphemes has 
also been associated with certain types of aphasic 
behavior in English speaking patients, speci- 
fically in agrammatics and Broca's aphasics. 
French neurolinguistic studies have documented a 
similar degradation in the ability of agrammatic 
and Broca's aphasics (LeCours and Lhermitte, 1969; 
Nespoulos, 1973; 1981; Segui, Mehler, Frauen- 
felder, and Morton, 1982; Tissot, Mounin, and 
Lhermitte, 1973). However, only the quantity of 
degradation is reported. The studies discuss 
performance in general and have not specifically 
addressed to what extent and in what ways these 
morphemes are affected as do some of the English 
studies (Zurif and Blumstein, 1978; Zurif, Green, 
Caramazza and Goodenough, 1976). 
Because of the import of articles in language 
processing, as briefly mentioned, how they are 
represented is of great interest when one wants to 
454 
use the adapted model, I'ESPERANCE, in its "le- 
sioned" state to study the linguistic results. 
Finally, to further illustrate the problems 
encountered in determining the phonetic repre- 
sentation, examples of the implications of de- 
ciding to represent the word for water, "eau," 
will be used. These implications are relevant to 
automatic speech recognition as well. 
The French equivalent for "some water" is "de 
l'eau" which includes the generic article, le, in 
an elision context. Water is spoken as l'eau even 
though there is another article as above. The 
question becomes should the phonetic representa- 
tion be defined as "l'eau" or as the content word 
in isolation, "eau?" The decision affects the 
homophone set association and will affect the 
entire across-time processing in any defined 
model. 
Current descriptions of research in automatic 
speech recognition for French (Pierrel, 1982; 
Quinton, 1982) provide no relevant information. 
The MYRTILLE II system described by Pierrel (1982) 
stresses use of linguistic knowledge and includes 
phonological substitutions for the same word. The 
system includes alternatives for words at their 
junction with other words in different phono- 
logical contexts. The system described by Quinton 
(1982), on the other hand, is very HEARSAY-like 
and does not specifically address how these mor- 
phemes are handled. 
Finally, the automatic translation work for 
French was consulted to see if there were any 
r~levant discussions included in the systems 
regarding the representations of words similar to 
"eau". In Ariane-78, article constraints are 
affixed as features to content words and elision 
is decided in the final stage of the production of 
the French sentences (Boitet and Nedobejkine, 
1981). The content words are specifically marked 
as beginning with vowels or silent "h". The final 
stage of the process joins the marked content word 
with an appropriate article to produce output 
words such as l'eau. This suggests that for 
comprehension, one would first recognize the unit 
"l'eau" and decompose it to the article and con- 
tent word with appropriate masculine/feminine 
indicators (Jayez, 1982). 
Initial assessment of the literature with 
respect to this problem has provided little evi- 
dence. The role of articles has not been studied 
for French to the extent that it has for English. 
Therefore, a pilot study with French aphasics was 
designed to analyze if and in what contexts these 
morphemes are affected. 
The study includes off-line picture naming 
which forces use of articles in all of the above 
contexts, as well as on-line production of these 
morphemes in an attempt to determine in which way 
these morphemes are related to the words. Are 
they unified with the word in all instances or 
only in certain contexts? 
Adapting a neurolinguistically motivated CN 
model for a second language can be seen to moti- 
vate a different type of question with regard to 
the second language than occurs when one bases the 
studies on English surface phenomena. This is 
very important because often surface phenomena are 
assumed to be more similar than warranted. What 
we claim instead is that the processing is 
similar, indeed universal and that we must begin 
to make cross-linguistic studies that assume this 
underlying commonality and at the same time can 
account for the variation at the surface level. 
5. SUMMARY 
Within developing computational neurolin- 
guistic research which assumes that we can define 
cognitively based simulation models using AI 
methodologies which are incorporated with neural 
processing paradigms, we have demonstrated how one 
can begin to study universals of language in a new 
perspective. 
The CN paradigm for natural language proces- 
sing includes claims that new perspectives on 
linguistically interpretable hierarchical repre- 
sentations that arise in language behavior are 
introduced by including neurally motivated pro- 
cessing control as the focus of model definition 
and by including behaviorially defined con- 
straints, both normal and pathological. 
The issues are not whether human brains work 
in a universal fashion, but instead raise ques- 
tions of how interpreted levels of representation, 
which functionally produce similar language be- 
havior need to be represented for different lan- 
guages. This processing approach includes many 
assumptions which are important to linguistic 
theory. Furthermore, it provides a way of de- 
veloping specific, verifiable questions about 
behavior which are mathematically better defined 
than through other methods, because it enables one 
to develop a broader perspective of the questions 
within an analysis of the hypothesis in the con- 
text of a characterization of the "how" of the 
entire behavior. 
By adapting HOPE for processing French, we 
furthermore claim that new perspectives on lan- 
guage universals are demonstrated. And finally, 
we feel that CN provides the only suitable way to 
begin developing a comprehensive understanding of 
a behavior as complex as language. 
6. REFERENCES 
Boitet, Ch. and Nedobejkine, N., Recent develop- 
ments in Russian-French machine translation at 
Grenoble. Linguistics, 19, 1981. 
Ballard, D.H., Parameter Nets. Technical Report 
TR75, Department of Computer Science, Univer- 
sity of Rochester, 1982. 
455 
Cottrell, G.W., A Connectionist Scheme for Model- 
ling Word Sense Disambiguation. Cognition an.__dd 
Brain Theory, 6, l, 1983. 
Feldman, J.A., A Connectionist Model of Visual 
Memory. Parallel Models of Associative Memory, 
G.E. Hinton, and J.A. Anderson (eds.), Lawrence 
Erlbaum Associates, Publishers, 1981. 
Gigley, H.M., Neurolinguistically Based Modeling 
of Natural Language Processing. Paper pre- 
sented at the Linguistic Society of America-- 
Association for Computational Linguistics 
Meeting, New York, 1981. 
Gigley, H.M., A Computational Neurolinguistic 
Approach to Processing Models of Sentence 
Comprehension. COINS Technical Report 82-9, 
Computer and Information Sciences Department, 
University of Massachusetts/Amherst, 1982. 
Gigley, H.M., Neurolinguistically Constrained 
Simulation of Sentence Comprehension: Inte- 
grating ArtTfic--Ta~ Intelligence and Brain 
Theory. Ph.D. Dissertation, Unive-~sity of 
Massachuetts/ Amherst, 1982. 
Gigley, H.M., Artificial Intelligence Meets Brain 
Theory: An Integrated Approach to Simulation 
Modelling of Natural Language Processing. 
Proceedings of the Sixth European Meeting on 
Cybernetics and Systems Research, H. Trappl 
(ed.), North-H-oTland, 1982. 
Gigley, H.M., HOPE-- AI and the Dynamic Process of 
Language Behavior. Cognition and Brain Theory, 
6, l, 1983. 
Gigley, H.M., Experiments in Artificial Aphasia -- 
Dynamics of Language Processing. Poster Ses- 
sion presented at the Academy of Aphasia, 
Minneapolis, 1983. 
Gigley, H.M., From HOPE en L'Esperance, Initial 
Investigation. Technical Report 84-24, Depart- 
ment of Computer Science, University of New 
Hampshire, 1984. 
Gigley, H.M., and Duffy, J.R., The Contribution of 
Clinical Intelligence and Artificial Aphasio- 
fogy to Clinical Aphasiology and Artificial 
Intelligence. Clinical Aphasiology, Proceed- 
ings of the Conference, R.H. Brookshire (ed.), 
Minneapolis, 1982. 
Goffic, P.L., and McBride, N.C., Les constructions 
fondamentales du francais. Libraries Hachette 
et Larousse, 1975. 
Gordon, B., Confrontation Naming: Computational 
Model and Disconnection Simulation. Neural 
Models of Language .Processes, M.A. Arbib, ~. 
Caplan, and J. Marshall (eds.), Academic Press, 
1982. 
Gross, M., Grammaire transformationnelle du 
francais: syntaxe du nom. Larousse, Paris, 
1977. 
Hayes, P.J., and Mouradian, G.V., Flexible Pars- 
ing. American Journal of Computational 
Linguistics, 7, 4, 1981. 
Jayez, J.-H., ComprEhension automatique du langage 
naturel. Masson, Paris, 1982. 
Kwasny, S.C., and Sondheimer, N.K., Relaxation 
Techniques for Parsing Ill-Formed Input. 
American Journal oi = Computational Linguistics, 
7, 2, 1981. 
Lavorel, P.M., Production Strategies: A Systems 
Approach to Wernicke's Aphasia. Neural Models 
of Language Processes, M.A. Arbib, D. Caplan, 
and J. Marshall (eds.), Academic Press, 1982. 
Lavorel, P.M., and Gigley, H.M., ElemEnts pour une 
th~orie gEnErale des machines intelligentes. 
Intellectica, Bulletin of the ASSOCIATION pour 
la RECHERECHE COGNITIVE, 7, Orsay, France, 
1983. 
Lecours, A.R., and Lehrmitte, F., Phonemic Para- 
phasias: Linguistic Structures and Tentative 
Hypotheses. CORTEX, 5, 1969. 
Lehnert, W.G., Dyer, M.G., Johnson, P.N. Yong, 
C.J., and Harley, S., BORIS--An Experiment in 
In-Depth Understanding of Narratives. Arti- 
ficial Intelligence, 20, 1983. 
McClelland, J.L. and Rumelhart, D.E., An Inter- 
active Activation Model of Context Effects in 
Letter Perception: Part I. An Account of Basic 
Findings. Psychological Review, 88, 5, 1981. 
Nespoulos, J.-L., Approche linguistique de divers 
phEnom~nes d'agrammatisme. ThEse 3rd cycle, 
UniversitE de Toulouse-le Mirail, Flammarion 
MEdecine-Sciences, Paris, 1973. 
Quinton, P., Utilisation de contraintes syn- 
taxiques pour la reconnaissance de la parole 
continue. Technique et Science Informatiques, 
l, 3, 1982. 
Reggia, J.A., and Sanjeev, B.A., Simulation of 
Phonemic Errors Using Artificial Intelligence 
Symbol Processing Techniques. Paper to be 
given at the Seventeenth Annual Simulation 
Symposium, 1984. 
Segui, J., Mehler, J., Frauenfelder, U., and 
Morton, J., The Word Frequency Effect and 
Lexical Access. Neuropsychologia, 20, 6, 1982. 
Small, S., Cottrell, G., and Shastri, L., Toward 
Connectionist Parsing. Proceedings of the 
National Conference on Artificial Intelligence, 
Pittsburgh, 1982. 
Tissot, R., Mounin, G., and Lhermitte, F., 
L'agrammatisme. Etude neuropsycholinguistique. 
Dessart, Bruxelles, 1973. 
Weischedel, R.M., and Black, J.E., If the Parse 
Fails. Proceedings of the 18th Annual Meeting 
of the Association for Computational Linguis- 
t~cs~nd Parasession on Topics in Interactive 
Discourse, Philadelphia, 1980. 
Wood, C.C., Variations on a theme by Lashley: 
Lesion experiments on the neural model of 
Anderson, Silverstein, Ritz, and Jones. 
Psychological Review, 85, 6, 1978. 
Wood, C.C., Interpretation of Real and Simulated 
Lesion Experiments. Psychological Review, 87, 
5, 1980. 
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