Using Commonsense Knowledge to Answer Why-Questions

Yash Kumar Lal, Niket Tandon, Tanvi Aggarwal, Horace Liu, Nathanael Chambers, Raymond Mooney, Niranjan Balasubramanian


Abstract
Answering questions in narratives about why events happened often requires commonsense knowledge external to the text. What aspects of this knowledge are available in large language models? What aspects can be made accessible via external commonsense resources? We study these questions in the context of answering questions in the TellMeWhy dataset using COMET as a source of relevant commonsense relations. We analyze the effects of model size (T5 and GPT3) along with methods of injecting knowledge (COMET) into these models. Results show that the largest models, as expected, yield substantial improvements over base models. Injecting external knowledge helps models of various sizes, but the amount of improvement decreases with larger model size. We also find that the format in which knowledge is provided is critical, and that smaller models benefit more from larger amounts of knowledge. Finally, we develop an ontology of knowledge types and analyze the relative coverage of the models across these categories.
Anthology ID:
2022.emnlp-main.79
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1204–1219
Language:
URL:
https://aclanthology.org/2022.emnlp-main.79
DOI:
10.18653/v1/2022.emnlp-main.79
Bibkey:
Cite (ACL):
Yash Kumar Lal, Niket Tandon, Tanvi Aggarwal, Horace Liu, Nathanael Chambers, Raymond Mooney, and Niranjan Balasubramanian. 2022. Using Commonsense Knowledge to Answer Why-Questions. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 1204–1219, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Using Commonsense Knowledge to Answer Why-Questions (Lal et al., EMNLP 2022)
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PDF:
https://aclanthology.org/2022.emnlp-main.79.pdf