@inproceedings{6acd1399385c4f809c02891129389628,
title = "On adversarial removal of hypothesis-only bias in natural language inference",
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.",
author = "Yonatan Belinkov and Adam Poliak and Shieber, {Stuart M.} and {Van Durme}, Benjamin and Alexander Rush",
note = "Publisher Copyright: {\textcopyright} 2019 Association for Computational Linguistics; 8th Joint Conference on Lexical and Computational Semantics, *SEM@NAACL-HLT 2019 ; Conference date: 06-06-2019 Through 07-06-2019",
year = "2019",
language = "الإنجليزيّة",
series = "*SEM@NAACL-HLT 2019 - 8th Joint Conference on Lexical and Computational Semantics",
pages = "256--262",
booktitle = "*SEM@NAACL-HLT 2019 - 8th Joint Conference on Lexical and Computational Semantics",
}