An Application of Answer Set Programming to the Field of Second Language Acquisition
This work addresses the need for computational tools to refine linguistic theories and support language teaching, though it is incremental as it applies an existing method (ASP) to a new domain.
The paper tackled the problem of formalizing Input Processing theory in Second Language Acquisition using Answer Set Programming (ASP) to predict how learners interpret English passive voice sentences, resulting in a system called PIas that assists language instructors in designing teaching materials.
This paper explores the contributions of Answer Set Programming (ASP) to the study of an established theory from the field of Second Language Acquisition: Input Processing. The theory describes default strategies that learners of a second language use in extracting meaning out of a text, based on their knowledge of the second language and their background knowledge about the world. We formalized this theory in ASP, and as a result we were able to determine opportunities for refining its natural language description, as well as directions for future theory development. We applied our model to automating the prediction of how learners of English would interpret sentences containing the passive voice. We present a system, PIas, that uses these predictions to assist language instructors in designing teaching materials. To appear in Theory and Practice of Logic Programming (TPLP).