Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation

@inproceedings{Poliak2018CollectingDN,
  title={Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation},
  author={Adam Poliak and Aparajita Haldar and Rachel Rudinger and J. Edward Hu and Ellie Pavlick and Aaron Steven White and Benjamin Van Durme},
  booktitle={EMNLP},
  year={2018}
}
We present a large-scale collection of diverse natural language inference (NLI) datasets that help provide insight into how well a sentence representation captures distinct types of reasoning. The collection results from recasting 13 existing datasets from 7 semantic phenomena into a common NLI structure, resulting in over half a million labeled context-hypothesis pairs in total. We refer to our collection as the DNC: Diverse Natural Language Inference Collection. The DNC is available online at… 

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