@InProceedings{poliak-EtAl:2018:S18-2,
  author    = {Poliak, Adam  and  Naradowsky, Jason  and  Haldar, Aparajita  and  Rudinger, Rachel  and  Van Durme, Benjamin},
  title     = {Hypothesis Only Baselines in Natural Language Inference},
  booktitle = {Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {180--191},
  abstract  = {We propose a hypothesis only baseline for diagnosing Natural Language Inference (NLI). Especially when an NLI dataset assumes inference is occurring based purely on the relationship between a context and a hypothesis, it follows that assessing entailment relations while ignoring the provided context is a degenerate solution. Yet, through experiments on 10 distinct NLI datasets, we find that this approach, which we refer to as a hypothesis-only model, is able to significantly outperform a majority-class baseline across a number of NLI datasets. Our analysis suggests that statistical irregularities may allow a model to perform NLI in some datasets beyond what should be achievable without access to the context.},
  url       = {http://www.aclweb.org/anthology/S18-2023}
}