Abstract
We present a scalable, low-bias, and low-cost method for building a commonsense inference dataset that combines automatic extraction from a corpus and crowdsourcing. Each problem is a multiple-choice question that asks contingency between basic events. We applied the proposed method to a Japanese corpus and acquired 104k problems. While humans can solve the resulting problems with high accuracy (88.9%), the accuracy of a high-performance transfer learning model is reasonably low (76.0%). We also confirmed through dataset analysis that the resulting dataset contains low bias. We released the datatset to facilitate language understanding research.- Anthology ID:
- 2020.emnlp-main.192
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2450–2460
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.192
- DOI:
- 10.18653/v1/2020.emnlp-main.192
- Cite (ACL):
- Kazumasa Omura, Daisuke Kawahara, and Sadao Kurohashi. 2020. A Method for Building a Commonsense Inference Dataset based on Basic Events. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 2450–2460, Online. Association for Computational Linguistics.
- Cite (Informal):
- A Method for Building a Commonsense Inference Dataset based on Basic Events (Omura et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2020.emnlp-main.192.pdf