Abstract
We define a novel textual entailment task that requires inference over multiple premise sentences. We present a new dataset for this task that minimizes trivial lexical inferences, emphasizes knowledge of everyday events, and presents a more challenging setting for textual entailment. We evaluate several strong neural baselines and analyze how the multiple premise task differs from standard textual entailment.- Anthology ID:
- I17-1011
- Volume:
- Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
- Month:
- November
- Year:
- 2017
- Address:
- Taipei, Taiwan
- Editors:
- Greg Kondrak, Taro Watanabe
- Venue:
- IJCNLP
- SIG:
- Publisher:
- Asian Federation of Natural Language Processing
- Note:
- Pages:
- 100–109
- Language:
- URL:
- https://aclanthology.org/I17-1011
- DOI:
- Cite (ACL):
- Alice Lai, Yonatan Bisk, and Julia Hockenmaier. 2017. Natural Language Inference from Multiple Premises. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 100–109, Taipei, Taiwan. Asian Federation of Natural Language Processing.
- Cite (Informal):
- Natural Language Inference from Multiple Premises (Lai et al., IJCNLP 2017)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/I17-1011.pdf
- Data
- SICK, SNLI