@InProceedings{belinkov-EtAl:2019:S19-1,
  author    = {Belinkov, Yonatan  and  Poliak, Adam  and  Shieber, Stuart  and  Van Durme, Benjamin  and  Rush, Alexander},
  title     = {On Adversarial Removal of Hypothesis-only Bias in Natural Language Inference},
  booktitle = {Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)},
  month     = {June},
  year      = {2019},
  address   = {Minneapolis, Minnesota},
  publisher = {Association for Computational Linguistics},
  pages     = {256--262},
  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.},
  url       = {http://www.aclweb.org/anthology/S19-1028}
}