Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation

Adam Poliak, Aparajita Haldar, Rachel Rudinger, J. Edward Hu, Ellie Pavlick, Aaron Steven White, Benjamin Van Durme
2018 Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP  
We present a large scale collection of diverse natural language inference (NLI) datasets that help provide insight into how well a sentence representation encoded by a neural network 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. Our collection of diverse datasets is available at http://www.decomp.net/, and will grow
more » ... over time as additional resources are recast and added from novel sources.
doi:10.18653/v1/w18-5441 dblp:conf/emnlp/PoliakHRHPWD18a fatcat:jgh6i4foxrdajbzjndcojer7mi