On Adversarial Removal of Hypothesis-only Bias in Natural Language Inference

Yonatan Belinkov, Adam Poliak, Stuart Shieber, Benjamin Van Durme, Alexander Rush


Abstract
Popular Natural Language Inference (NLI) datasets have been shown to be tainted by hypothesis-only biases. Adversarial learning may help models ignore sensitive biases and spurious correlations in data. We evaluate whether adversarial learning can be used in NLI to encourage models to learn representations free of hypothesis-only biases. Our analyses indicate that the representations learned via adversarial learning may be less biased, with only small drops in NLI accuracy.
Anthology ID:
S19-1028
Volume:
Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
256–262
Language:
URL:
https://www.aclweb.org/anthology/S19-1028
DOI:
10.18653/v1/S19-1028
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S19-1028.pdf