AAAI 2021 paper "Knowledge-Driven Distractor Generation for Cloze-style Multiple-choice Questions"
URL: http://202.120.38.146:10090/wordnet_dgen
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input: { "sentence": "The attraction between all objects in the universe is known as blank", "answer": "gravity", "num": 3 }
result { "requested_k": 3, "source": "WordNet", "sent": "The attraction between all objects in the universe is known as blank", "ans": "gravity", "distractors": [ "magnetism", "tension", "stress" ] }
How to cite
@article{Ren_Q. Zhu_2021,
title={Knowledge-Driven Distractor Generation for Cloze-Style Multiple Choice Questions},
volume={35},
url={https://ojs.aaai.org/index.php/AAAI/article/view/16559},
abstractNote={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.},
number={5},
journal={Proceedings of the AAAI Conference on Artificial Intelligence},
author={Ren, Siyu and Q. Zhu, Kenny},
year={2021},
month={May},
pages={4339-4347} }