Temporal Reasoning in Natural Language Inference
Siddharth Vashishtha, Adam Poliak, Yash Kumar Lal, Benjamin Van Durme, Aaron Steven White
Abstract
We introduce five new natural language inference (NLI) datasets focused on temporal reasoning. We recast four existing datasets annotated for event duration—how long an event lasts—and event ordering—how events are temporally arranged—into more than one million NLI examples. We use these datasets to investigate how well neural models trained on a popular NLI corpus capture these forms of temporal reasoning.- Anthology ID:
- 2020.findings-emnlp.363
- Original:
- 2020.findings-emnlp.363v1
- Version 2:
- 2020.findings-emnlp.363v2
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2020
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4070–4078
- Language:
- URL:
- https://aclanthology.org/2020.findings-emnlp.363
- DOI:
- 10.18653/v1/2020.findings-emnlp.363
- Cite (ACL):
- Siddharth Vashishtha, Adam Poliak, Yash Kumar Lal, Benjamin Van Durme, and Aaron Steven White. 2020. Temporal Reasoning in Natural Language Inference. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 4070–4078, Online. Association for Computational Linguistics.
- Cite (Informal):
- Temporal Reasoning in Natural Language Inference (Vashishtha et al., Findings 2020)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2020.findings-emnlp.363.pdf
- Code
- sidsvash26/temporal_nli
- Data
- MultiNLI