Yes! NLP-based FL-ITS will be Important 
Henry Hamburger 
Department of Computer Science 
George Mason University 
Fairfax VA 22030 USA 
henryh@cs.gmu.edu 
In this note, a straw man is destroyed, optimism is 
expressed, an existing system is sketched, and some 
issues are laid out. 
A Direct Approach. 
It is useful to consider a straightforward argument 
for according NLP a central role in CALL. I will 
call it SCW, since it is simple, clear and wrong. It 
runs like this: intelligent tutoring systems (ITS) 
with simulated problem environments are 
potentially excellent for learning; a key module of 
an ITS is a computational model of expertise in the 
domain; NLP systems are such models in the 
domain of hmnan languages; so let's use NLP 
knowledge bases as the expert module of a foreign 
language ITS. 
see Chanier et al. (1992). For a rule to be learned or 
even to be useful in explanation, it should be 
congruent with learners' cognition and expressed in 
a way that is meaningful to them. Anyone familiar 
with NI,P knowledge bases could hardly think 
seriously about promoting their direct use in 
explanations to learners. 
Stay the Course. 
The lailure of SCW certainly does not mean we 
should all go home. On the contrary, I strongly 
believe that valuable ITSs will be built and that 
they will use NLP, or I would not spend so much of 
my time on one. It does, however, suggest that it 
will take significant work, in design as well as 
implementation, to create an NLP-driven ITS for 
language that engages students and helps ttlem 
learn efficiently. 
But Language is Different. 
But language use is different fl'om most ITS 
domains, in a way that invalidates SCW. In a 
typical domain a student shows progress by success 
in explicit stepwise reasoning to a solution. 
Successful language use, in contrast both to other 
ITS domains and to the ability to state grammatical 
rules, need not demand articulating the stepwise 
reasoning about sentence construction, but it does 
require the capacity to arrive quickly at a result 
(either a sentence that expresses one's current 
thought or the meaning of someone else's 
sentence), and moreover to do so while thinking 
about something else, namely the substance of the 
conversation. Such a capacity may well imply a 
grammar in the brain but not awareness and 
articulation of it. 
Expertise and ITS. 
Even for explicit teaching of grammatical, lexical 
or other knowledge of a particular language, SCW 
is flawed. Experience teaches that a performance- 
oriented representation of domain knowledge may 
be tmsuitable tbr direct use in an ITS; see Ciancey 
(1987) and in the world of language specifically, 
Let Many Flowers Bloom. 
Proclaiming a future for NLP in ITS is not to deny 
benefits from other kinds of devices or the 
continuing importance of hmnan teachers, tutors 
and other conversational partners. Indeed N1,P 
itself may have variegated ways of contributing to 
the mix of learning resources, by systems that use 
different modules of NLP and that aim to foster 
various kinds of language knowledge in the 
student. Automated aid has been undertaken lot 
parts of languages all the way from spelling, 
pronunciation and morphology, through syntax and 
semantics, to discourse and cultural knowledge. For 
the most part, the NLP-based work has been at the 
low end, rarely going above syntax. 
Two-Medium Conversation. 
A sketch of our own (Hamburger, 1995) two- 
medium conversational system will give a concrete 
sense of one approach to NLP-intensive CALL. 
FLUENT-2 is a language learning and tutoring 
system, helping students learn and letting teachers 
influence what is learned. It has features of both 
intelligent tutoring systems and microworld 
learning environments. Using a pedagogical 
1007 
strategy of situational immersion, the system 
engages the student in meaningful multi-media 
communicative acts in graphically depicted real 
world situations. 
The student interacts with the system by direct use 
of the target language. The system uses Felshin's 
(1993) multilingual NLP system, which can expose 
students to a wide variety of linguistic forms. The 
intent is that new words, phrases and grammatical 
usage will become comprehensible through 
meaningful exposure and use. The system makes 
this possible by tightly coupling the language to 
graphical acts and system generated animations 
within a realistic ongoing situation. Actions 
available to both the student and the system include 
selecting and moving objects and making human 
figures walk, turn, point, grasp and release objects, 
and so on. 
The teacher interacts with the system through 
graphical (GUI) tools that facilitate the designing of 
exercises and the construction of appropriate 
microworlds. These tools let a teacher construct 
exercises that invoke specific linguistic concepts in 
the target language without having to deal directly 
with the NLP system. One parameter of an exercise 
can be a plan for a goal in a situation, a capacity 
that makes exercises portable across microworlds. 
Real language teachers have given design advice 
and now use the system. 
Three Issues. 
Fidelity and interactiveness are key issues tbr ITS. 
Extensibility has been a key issue in NLP. All three 
will be important for NLP-based CALL. I'll 
subdivide each, mention some interplay among 
them and comment on Fluent-2 in light of them. 
Fidelity is the accuracy of a presentation. For 
example the visual fidelity of a photo or video 
exceeds that of simple graphics or animation. A 
technical drawing may have good conceptual 
fidelity if it connects related concepts. Two aspects 
of fidelity that may rightly concern language 
educators are cultural authenticity and the 
situational continuity of a conversation. 
Interactiveness of an ITS can include its immediate 
responsiveness by faithfully updating the visible 
situation after a student makes some kind of move 
as well as longer-range responsiveness to an 
individual student, based on a model that it builds 
of that student's knowledge. In addition, the student 
may be offered control over parameters of the 
systems behavior, including subject matter, 
difficulty and style. We have used this last 
approach, in response to arguments made by Self 
(1988). 
A system can be designed to be extendable within a 
language. It takes extra effort to make it possible 
for a teacher to do so, as opposed to a programmer. 
Portability across languages is familiar to NLP 
researchers, and, as noted above, portability can 
also refer to moving exercise types into new 
situations. Schoelles and Hamburger (1996) show 
how this capacity lets one present a language 
concept in one situation and test it in another. 
There are tradeoffs here. As an example, Murray's 
(1995) video system achieves exceptionally fine 
cultural fidelity, but for that reason little of it is 
language portable. Interactiveness also is difficult 
to achieve with video, since there is a finite amount 
of video material produced in advance. Our 
generative, recombinative animation approach does 
not encounter this constraint. 
In the End. 
It will not be easy, but I hope and believe that 
before too long there will be a variety of exciting, 
effective NLP-based CALL systems. 
References 
Chanier, T., Pengelly, M., Twidale, M. and Self, J. 
(1992) Conceptual modelling in error analysis in 
computer-assisted language learning systems. In 
Swartz, M.L. and Yazdani, M. (Eds.) Intelligent 
Tutoring Systems for Foreign Language Learning. 
Berlin: Springer-Verlag 
Clancey, W. (1987) Knowledge-Based Tutoring: 
The GUIDON Program. Cambridge, MA, USA: 
MIT Press. 
Felshin, S. (1993) A Guide to the Athena Language 
Learning Project Natural Language Processing 
Systems. MIT. 
Hamburger, H. (1995) Tutorial tools for language 
learning by two-medium dialogue. In Holland, 
V.M., Kaplan, J.D. and Sams, M.R. (Eds.) 
Intelligent Language Tutors. Mahwah, NJ, USA: 
Erlbaum. 
Murray, J. (1995) Lessons learned from the Athena 
Language Learning Project. In Holland, V.M., 
Kaplan, J.D. and Sams, M.R. (Eds.) Intelligent 
Language Tutors, Mahwah, NJ, USA: Erlbaum. 
1008 
Schoelles, M. and Hamburger, H. (1996) Teacher- 
usable exercise design tools. Montreal: Proceedings 
of ITS-96. 
Self, J.A. (1988) Bypassing the intractable problem 
of student modelling. Montreal: ITS-88. 
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