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

Authors

  • Siyu Ren Shanghai Jiao Tong University
  • Kenny Q. Zhu Shanghai Jiao Tong University

Keywords:

Applications, Education

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.

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Published

2021-05-18

How to Cite

Ren, S., & Q. Zhu, K. (2021). Knowledge-Driven Distractor Generation for Cloze-Style Multiple Choice Questions. Proceedings of the AAAI Conference on Artificial Intelligence, 35(5), 4339-4347. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16559

Issue

Section

AAAI Technical Track on Data Mining and Knowledge Management