The NLP Role in Animated Conversation for CALL

The NLP Role in Animated Conversation for CALL Michael Schoelles and Henry Hamburger Dept. of Computer Science George Mason University Fairfax, VA 22030 USA mikejs@cs.gmu.edu & henryh@cs.gmu.edu Abstract Language learning is a relatively new appli- cation for natural language processing (NLP) and for intelligent tutoring and learning environments (ITLEs). NLP has a crucial role to play in foreign language ITLEs, whether they are designed for explicit or implicit learning of the vocabulary and grammar. FLUENT is an implicit approach, in which NLP and shared- control animation support a two-medium conversation, designed to foster implicit learning of language. This report highlights specific ways that FLUENT already uses NLP, suggests potential benefits from additional use of NLP and grounds the method in widely used language learning pedagogy. It concludes by describing and evaluating the system's use in the classroom, with a particularly challenging kind of learner. 1 Introduction Language learning is a relatively new application for natural language processing (NLP), compared to translation and database interfaces. The 1988 Trieste conference on foreign language intelligent tutoring systems did not manage a proceedings, so few were the functioning systems using NLP and other techniques of artificial intelligence (AI). Indeed the field of Computer-Assisted Language Learning (CALL) has been dominated by the work of language teachers who, tired of waiting for us, have proceeded to learn some programming or hire computer science students to make their own systems, most of them just for fairly inflexible drill-and-practice. The 1990s have witnessed increasing interest in bringing AI to language learning systems, reflected in Chanier's (1994) special issue, Holland's anthology (1995) and Zock's (1996) panel. The AI is principally of two kinds: NLP and what we will call ITLE, the overlapping categories of intelligent tutoring systems and interactive learning environments. In this paper, we report on a foreign language ITLE that has moved from the lab into the classroom. After noting alternative strategies for using NLP in CALL (section 2), we describe our system and its NLP require- ments (section 3), as well as its pedagogical foundations (section 4). Finally, we describe its graduation into the classroom, for use with a particularly challenging kind of learner: the highly motivated but computer- unfamiliar immigrant. 2 A Crucial Role for NLP in CALL Developers of foreign language ITLEs have only recently begun to expand the use of NLP in language learning systems beyond relatively simple uses of syntax. More sophisticated and widespread use has awaited greater robustness and ease of use of NLP software as well as better speed and memory of the hardware. Recent improvements along these lines are playing key roles in meeting the special NLP requirements of foreign language ITLEs. Since the language is the subject matter to be learned, ITLEs must expect the input to be in error, and therefore require very robust NLP systems with error detection capabilities. In addition, conversational ITLEs must respond to student input in a time frame that parallels natural dialog. Advances in parsing technology and faster machines with more memory …

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