Abstract
Reasoning about tabular information presents unique challenges to modern NLP approaches which largely rely on pre-trained contextualized embeddings of text. In this paper, we study these challenges through the problem of tabular natural language inference. We propose easy and effective modifications to how information is presented to a model for this task. We show via systematic experiments that these strategies substantially improve tabular inference performance.- Anthology ID:
- 2021.naacl-main.224
- Volume:
- Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Month:
- June
- Year:
- 2021
- Address:
- Online
- Editors:
- Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, Yichao Zhou
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2799–2809
- Language:
- URL:
- https://aclanthology.org/2021.naacl-main.224
- DOI:
- 10.18653/v1/2021.naacl-main.224
- Cite (ACL):
- J. Neeraja, Vivek Gupta, and Vivek Srikumar. 2021. Incorporating External Knowledge to Enhance Tabular Reasoning. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2799–2809, Online. Association for Computational Linguistics.
- Cite (Informal):
- Incorporating External Knowledge to Enhance Tabular Reasoning (Neeraja et al., NAACL 2021)
- PDF:
- https://preview.aclanthology.org/fix-dup-bibkey/2021.naacl-main.224.pdf
- Code
- utahnlp/knowledge_infotabs
- Data
- InfoTabS, MultiNLI, TabFact