Exploring Lexical Irregularities in Hypothesis-Only Models of Natural Language Inference

@article{Hu2020ExploringLI,
  title={Exploring Lexical Irregularities in Hypothesis-Only Models of Natural Language Inference},
  author={Qingyuan Hu and Yi Zhang and Kanishka Misra and Julia Taylor Rayz},
  journal={2020 IEEE 19th International Conference on Cognitive Informatics \& Cognitive Computing (ICCI*CC)},
  year={2020},
  pages={125-130}
}
  • Qingyuan HuYi Zhang J. Rayz
  • Published 26 September 2020
  • Computer Science
  • 2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)
Natural Language Inference (NLI) or Recognizing Textual Entailment (RTE) is the task of predicting the entailment relation between a pair of sentences (premise and hypothesis). This task has been described as “a valuable testing ground for the development of semantic representations” [1], and is a key component in natural language understanding evaluation benchmarks. Models that understand entailment should encode both, the premise and the hypothesis. However, experiments by Poliak et al. [2… 

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This approach, which is referred to as a hypothesis-only model, is able to significantly outperform a majority-class baseline across a number of NLI datasets and suggests that statistical irregularities may allow a model to perform NLI in some datasets beyond what should be achievable without access to the context.

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