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


Abstract
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 over time as additional resources are recast and added from novel sources.
Anthology ID:
W18-5441
Volume:
Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP
Month:
November
Year:
2018
Address:
Brussels, Belgium
Venues:
EMNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
337–340
Language:
URL:
https://aclanthology.org/W18-5441
DOI:
10.18653/v1/W18-5441
Bibkey:
Cite (ACL):
Adam Poliak, Aparajita Haldar, Rachel Rudinger, J. Edward Hu, Ellie Pavlick, Aaron Steven White, and Benjamin Van Durme. 2018. Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation. In Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, pages 337–340, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation (Poliak et al., 2018)
Copy Citation:
PDF:
https://aclanthology.org/W18-5441.pdf
Video:
 https://vimeo.com/305194062
Data
FrameNetGLUEMultiNLISNLI