Automatically generating challenging distractors for multiple-choice gap-fill items is still an unsolved problem. We propose to employ context-sensitive lexical inference rules in order to generate distractors that are semantically similar to the gap target word in some sense, but not in the particular sense induced by the gap-fill context. We hypothesize that such distractors should be particularly hard to distinguish from the correct answer. We focus on verbs as they are especially difficult to master for language learners and find that our approach is quite effective. In our test set of 20 items, our proposed method decreases the number of invalid distractors in 90% of the cases, and fully eliminates all of them in 65%. Further analysis on that dataset does not support our hypothesis regarding item difficulty as measured by average error rate of language learners. We conjecture that this may be due to limitations in our evaluation setting, which we plan to address in future work.
REF generate distractors that are semantically similar to the target word in some sense, but not in the particular sense induced by the gap-fill context.
Automatic Generation of Challenging Distractors Using Context-Sensitive Inference Rules
BEA@ACL
Computer Science