A Semantic-based Method for Unsupervised Commonsense Question Answering

Yilin Niu, Fei Huang, Jiaming Liang, Wenkai Chen, Xiaoyan Zhu, Minlie Huang


Abstract
Unsupervised commonsense question answering is appealing since it does not rely on any labeled task data. Among existing work, a popular solution is to use pre-trained language models to score candidate choices directly conditioned on the question or context. However, such scores from language models can be easily affected by irrelevant factors, such as word frequencies, sentence structures, etc. These distracting factors may not only mislead the model to choose a wrong answer but also make it oversensitive to lexical perturbations in candidate answers. In this paper, we present a novel SEmantic-based Question Answering method (SEQA) for unsupervised commonsense question answering. Instead of directly scoring each answer choice, our method first generates a set of plausible answers with generative models (e.g., GPT-2), and then uses these plausible answers to select the correct choice by considering the semantic similarity between each plausible answer and each choice. We devise a simple, yet sound formalism for this idea and verify its effectiveness and robustness with extensive experiments. We evaluate the proposed method on four benchmark datasets, and our method achieves the best results in unsupervised settings. Moreover, when attacked by TextFooler with synonym replacement, SEQA demonstrates much less performance drops than baselines, thereby indicating stronger robustness.
Anthology ID:
2021.acl-long.237
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3037–3049
Language:
URL:
https://aclanthology.org/2021.acl-long.237
DOI:
10.18653/v1/2021.acl-long.237
Bibkey:
Cite (ACL):
Yilin Niu, Fei Huang, Jiaming Liang, Wenkai Chen, Xiaoyan Zhu, and Minlie Huang. 2021. A Semantic-based Method for Unsupervised Commonsense Question Answering. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 3037–3049, Online. Association for Computational Linguistics.
Cite (Informal):
A Semantic-based Method for Unsupervised Commonsense Question Answering (Niu et al., ACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-long.237.pdf
Video:
 https://aclanthology.org/2021.acl-long.237.mp4
Data
ATOMICCOPAConceptNetSocial IQA