Knowledge-Driven Distractor Generation for Cloze-style Multiple Choice Questions [article]

Siyu Ren, Kenny Q. Zhu
2020 arXiv   pre-print
In this paper, we propose a novel configurable framework to automatically generate distractive choices for open-domain cloze-style multiple-choice questions, which incorporates a general-purpose knowledge base to effectively create a small distractor candidate set, and a feature-rich learning-to-rank model to select distractors that are both plausible and reliable. Experimental results on datasets across four domains show that our framework yields distractors that are more plausible and
more » ... than previous methods. This dataset can also be used as a benchmark for distractor generation in the future.
arXiv:2004.09853v3 fatcat:rmdmsonfxfcwnd6sl5jy4z6d7y