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.
BibTeX
@inproceedings{luo-etal-2022-masked,
title = "Masked Language Models Know Which are Popular: A Simple Ranking Strategy for Commonsense Question Answering",
author = "Luo, Xuan and
Fan, Chuang and
Zhang, Yice and
Jiang, Wanguo and
Qin, Bing and
Xu, Ruifeng",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.233/",
doi = "10.18653/v1/2022.findings-emnlp.233",
pages = "3200--3213"
}