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This paper reports on the recognition component of an intelligent tutoring system that is designed to help foreign language speakers learn standard English. The system models the grammar of the learner, with this instantiation of the system tailored to signers of American Sign Language (ASL). We discuss the theoretical motivations for the system, various difficulties that have been encountered in the implementation, as well as the methods we have used to overcome these problems. Our method of capturing ungrammaticalities involves using malrules (also called 'error productions'). . | Recognizing Syntactic Errors in the Writing of Second Language Learners David Schneider Department of Linguistics University of Delaware Newark DE 19716 and Kathleen F. McCoy Computer and Information Sciences University of Delaware Newark DE 19716 dschneid mccoy @cis. udel.edu Abstract This paper reports on the recognition component of an intelligent tutoring system that is designed to help foreign language speakers learn standard English. The system models the grammar of the learner with this instantiation of the system tailored to signers of American Sign Language ASL . We discuss the theoretical motivations for the system various difficulties that have been encountered in the implementation as well as the methods we have used to overcome these problems. Our method of capturing ungrammaticalities involves using mal-rules also called error productions . However the straightforward addition of some mal-rules causes significant performance problems with the parser. For instance the ASL population has a strong tendency to drop pronouns and the auxiliary verb to be . Being able to account for these as sentences results in an explosion in the number of possible parses for each sentence. This explosion left unchecked greatly hampers the performance of the system. We discuss how this is handled by taking into account expectations from the specific population some of which are captured in our unique user model . The different representations of lexical items at various points in the acquisition process are modeled by using mal-rules which obviates the need for multiple lexicons. The grammar is evaluated on its ability to correctly diagnose agreement problems in actual sentences produced by ASL native speakers. 1 Overview This paper reports on the error-recognition component of the ICICLE Interactive Computer Identification and Correction of Language Errors system. The system is designed to be a tutorial system for helping second-language L2 learners of English. In this .
