Masked Language Models Know Which are Popular: A Simple Ranking Strategy for Commonsense Question Answering

Xuan Luo, Chuang Fan, Yice Zhang, Wanguo Jiang, Bing Qin, Ruifeng Xu


Abstract
We propose a simple ranking strategy to solve a generative commonsense question answering (QA) problem. Compared with multiple-choice QA, it is challenging because the answers to a question are not unique and they are supposed to be popular and diverse. Our strategy exploits the dataset itself and negative samples that we collect from WordNet to train a ranker that picks out the most popular answers for commonsense questions. The effectiveness of our strategy is verified on different pre-trained masked language models (MLMs) in a pipeline framework, where an MLM reranks the generated answers. Further, we explore an end-to-end framework where MLMs are utilized to guide the generation of generative language models (GLMs). Taking advantage of reinforcement learning, we apply policy gradient to train a GLM with the rewards fed back by an MLM. Empirical results on ProtoQA dataset demonstrate that MLMs can acquire the ability to distinguish the popular answers and improve the typical answer generation of GLMs as well.
Anthology ID:
2022.findings-emnlp.233
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2022
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3200–3213
Language:
URL:
https://aclanthology.org/2022.findings-emnlp.233
DOI:
10.18653/v1/2022.findings-emnlp.233
Bibkey:
Cite (ACL):
Xuan Luo, Chuang Fan, Yice Zhang, Wanguo Jiang, Bing Qin, and Ruifeng Xu. 2022. Masked Language Models Know Which are Popular: A Simple Ranking Strategy for Commonsense Question Answering. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 3200–3213, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Masked Language Models Know Which are Popular: A Simple Ranking Strategy for Commonsense Question Answering (Luo et al., Findings 2022)
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