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
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
9106–9118
Language:
URL:
https://www.aclweb.org/anthology/2020.emnlp-main.732
DOI:
10.18653/v1/2020.emnlp-main.732
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.emnlp-main.732.pdf