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.
Cite
Text
Ren and Zhu. "Knowledge-Driven Distractor Generation for Cloze-Style Multiple Choice Questions." AAAI Conference on Artificial Intelligence, 2021. doi:10.1609/AAAI.V35I5.16559Markdown
[Ren and Zhu. "Knowledge-Driven Distractor Generation for Cloze-Style Multiple Choice Questions." AAAI Conference on Artificial Intelligence, 2021.](https://mlanthology.org/aaai/2021/ren2021aaai-knowledge/) doi:10.1609/AAAI.V35I5.16559BibTeX
@inproceedings{ren2021aaai-knowledge,
title = {{Knowledge-Driven Distractor Generation for Cloze-Style Multiple Choice Questions}},
author = {Ren, Siyu and Zhu, Kenny Q.},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2021},
pages = {4339-4347},
doi = {10.1609/AAAI.V35I5.16559},
url = {https://mlanthology.org/aaai/2021/ren2021aaai-knowledge/}
}