An Analysis of Natural Language Inference Benchmarks through the Lens of Negation

Md Mosharaf Hossain, Venelin Kovatchev, Pranoy Dutta, Tiffany Kao, Elizabeth Wei, Eduardo Blanco


Abstract
Negation is underrepresented in existing natural language inference benchmarks. Additionally, one can often ignore the few negations in existing benchmarks and still make the right inference judgments. In this paper, we present a new benchmark for natural language inference in which negation plays a critical role. We also show that state-of-the-art transformers struggle making inference judgments with the new pairs.
Anthology ID:
2020.emnlp-main.732
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:
9106–9118
Language:
URL:
https://aclanthology.org/2020.emnlp-main.732
DOI:
10.18653/v1/2020.emnlp-main.732
Bibkey:
Cite (ACL):
Md Mosharaf Hossain, Venelin Kovatchev, Pranoy Dutta, Tiffany Kao, Elizabeth Wei, and Eduardo Blanco. 2020. An Analysis of Natural Language Inference Benchmarks through the Lens of Negation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 9106–9118, Online. Association for Computational Linguistics.
Cite (Informal):
An Analysis of Natural Language Inference Benchmarks through the Lens of Negation (Hossain et al., EMNLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.732.pdf
Video:
 https://slideslive.com/38939113