Generating grammar questions using corpus data in L2 learning

Abstract

This paper examines how grammar questions are automatically generated for L2 learning by applying a sequential labeling technique to learner corpora. We developed a model that helps detect possible error positions and select the most appropriate form among choices. Discriminant models such as conditional random field and maximum entropy are used to generate the error identification question. Questions generated by the proposed method corresponded highly to questions that experts made. Our data-driven approach lends itself to any language without costing expensive expertise.