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
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 337–340
- Language:
- URL:
- https://aclanthology.org/W18-5441
- DOI:
- 10.18653/v1/W18-5441
- 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., EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/W18-5441.pdf
- Data
- FrameNet, GLUE, MultiNLI, SNLI