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 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 https://www.decomp.net, and will grow over time as additional resources are recast and added from novel sources.
Anthology ID:
D18-1007
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
67–81
Language:
URL:
https://aclanthology.org/D18-1007
DOI:
10.18653/v1/D18-1007
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 Conference on Empirical Methods in Natural Language Processing, pages 67–81, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation (Poliak et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1007.pdf
Video:
 https://vimeo.com/305194062