We describe an approach to grammatical error diagnosis in computer-assisted language instruction (CALI). Our prototype system, Scripsi, employs a model of the linguistic competence of the second language learner in diagnosing ungrammaticality in learners' writing. Scripsi not only detects errors, but hypothesises their cause and provides corrective information to the student.
Scripsi's grammatical model reflects the results of research in second language acquisition, which has identified language transfer and rule overgeneralisation as the chief sources of error in learner language. Thus, in characterizing the learner's ``transitional competence'', we model not only the grammar of the learner's native language, but also the strategies that give rise to overgeneralisation. Although the approach is language-independent, our implementation targets French-speaking and Chinese-speaking learners of English.
The computational realization of the model assumes that linguistic behaviour is rule-governed. We have adopted a rule-oriented grammatical formalism in which the processes of transfer and overgeneralisation are readily interpreted. Linguistic rules are expressed in a feature-based grammatical framework closely related to the Standard Theory of transformational grammar. We have extended the shift-reduce parsing algorithm in order to accommodate context-sensitive and transformational aspects of the formalism.
We argue that the development of expertise in intelligent grammatical diagnosis is a prerequisite for the next generation of CALI tools -- genuinely communicative systems capable of interacting linguistically with the student.
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