Computational Linguistics and its Use in Real World: 
the Case of Computer Assisted-Language Learning 
Michael ZOCK 
Langage & Cognition 
LIMSI-CNRS, B. P. 133 
91403 ORSAY - FRANCE 
e-mail:zock@limsi.fr 
Surprising as it may be, one of the biggest markets 
for products of computional linguistics (CL) has been 
largely overlooked: the classroom. While machine 
translation has attracted a considerable amount of 
research, hence resources, CALL, ~ a domain with a 
comparable potential, has hardly ever received the 
attention it deserves. Actually there seems to be a 
communication problem and a mutual lack of interest 
concerning the work done in the neighbouring 
disciplines. 
Computational linguists don't show much interest 
for CALL, and CALL experts ignore the work done 
by computational linguists. Strangely enough, even 
within the ITS, CAI, CBI OR ICAI communities, 
little, if any reference is made to work done in CALL 
(4, 10, 13, 15, 18, 24, 25, 26, 28, 30, 33, 35). This 
being so, it is hardly surprising to see that the domain 
is never mentioned in textbook on Artificial Intelli- 
gence or Psycholinguistics. Yet there are a number of 
publications in psychology that deal with related 
issues such as learning theory (12), language 
learning (5, 6, 11, 14, 20), language teaching (6, 16, 
19, 27), educational technology, i.e. programmed 
instruction (9, 12, 31), theory of writing (3), 
algorithmization of the learning process (17), 
learning strategies, i.e. learning how to learn (23), 
etc. 
It is also worth mentionning that no cross fertili- 
zation has taken place between the CALL community 
and people working in the Machine Learning 
paradigm (7, 21). 2 While there are fundamental 
differences in terms of goals and methods, there are 
also some important overlaps. Books on more 
sophisticated CALL systems are still scarce (13, 32), 
so is the work that shows how current NLP 
technology could be used in the classroom (1, 22, 34) 
Yet CALL is a field with considerable potential. It 
is both a challenge and a chance to bring NLP 
' Unlike 17S (Intelligent Teaching Systems), CAI (Com- 
puter Assisted Instruction), CBI (Computer Based Instruc- 
tion) and ICAI (Intelligent Computer Aided Instruction), 
which use language \['or communicating domain specific 
knowledge, CALL has langage learning as its primary goal. 
Obviously, NLP-technology may be relevant for all these 
systems, but in different ways. 
For a slightly outdated bibliography on CALL, see (2). 
technology from the research laboratories to the real 
world. While computational linguists will certainly 
have to play an important role in providing linguistic 
resources (grammars, lexicon) and processing tools, 
it is not clear yet how to decide on the adequacy of 
the tools (browser, editors). Also, there are good 
chances that within this context new problems arise, 
while old solution turn out not to be good at all, in 
which case the following two questions arise: what is 
the nature of these new problems?, and in what terms 
do these new problems have to be rethought? Other 
related issues of interest are the following: 
• what can current NLP technology contribute to 
computer-assisted language learning? 
how can this technology meet the demands of 
pedagogical theory for communicative language 
teaching in a natural environment? 
what can NLP-based systems teach us about 
language acquisition, linguistic theory and 
NATURAL language processing in general? 
what effect can a domain like CALL, or the 
involved disciplines have on the development of 
NLP technology? 
• what lessons have been, or can be learned by 
looking at the available CALL systems? 
In order to get a clearer picture of these problems, 
and in order to draw the community's attention to the 
fact that there is a REAL need and potential for 
integrating NLP technologies in CALL systems, we 
propose a panel discussion between specialists in the 
concerned disciplines (linguistics, artificial 
intelligence, psychology, language teaching). The 
expected results of such a discussion are not only an 
increase of resources (manpower) in the CALL 
domain, but also an increase of awareness, that is, a 
sharpening of the researchers' understanding of what 
the problems are that people encounter when 
processing language. All too often we look at 
language only from the point of view of the machine, 
i.e. how can languages be processed by computers. In 
doing so we tend to forget the obvious : natural 
languages are used by people. 
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In building CALl, systems we will realize that 
there are ninny problems in the area of natural 
language processing that have been either 
overlooked, or been posed in inadequate terms. Yet, 
if we really want to get a real tmderstanding of the 
fimctioning of natural languages, --how they are 
used, how they are learned?-- we have to look at the 
constraints of the system for which they have been 
designed: man. This is the price we have to pay if we 
want to produce programs that are of interest not only 
in the research labs but also into the arena of real 
world. 
Strangely enough, in the past we had neither the 
right tools, nor a decent theory (see 8, 27, 31), yet 
people were optimistic and went ahead. Today we are 
much better off. We do have very powerfid tools (fast 
computers with well designed graphical interfaces, 
browsers, CD-Roms, authoring languages), and a 
whole set of quite promising theories, yet we hesitate. 
But, what are we waiting lot'? 
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