Knowledge-Driven Distractor Generation for Cloze-Style Multiple Choice Questions

Abstract

In this paper, we propose a novel configurable framework to automatically generate distractive choices for open-domain cloze-style multiple-choice questions. The framework 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 a new dataset across four domains show that our framework yields distractors outperforming previous methods both by automatic and human evaluation. The dataset can also be used as a benchmark for distractor generation research in the future.