Abstract
We consider the task of relation classification, and pose this task as one of textual entailment. We show that this formulation leads to several advantages, including the ability to (i) perform zero-shot relation classification by exploiting relation descriptions, (ii) utilize existing textual entailment models, and (iii) leverage readily available textual entailment datasets, to enhance the performance of relation classification systems. Our experiments show that the proposed approach achieves 20.16% and 61.32% in F1 zero-shot classification performance on two datasets, which further improved to 22.80% and 64.78% respectively with the use of conditional encoding.- Anthology ID:
- W18-5511
- Volume:
- Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)
- Month:
- November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- James Thorne, Andreas Vlachos, Oana Cocarascu, Christos Christodoulopoulos, Arpit Mittal
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 72–78
- Language:
- URL:
- https://aclanthology.org/W18-5511
- DOI:
- 10.18653/v1/W18-5511
- Cite (ACL):
- Abiola Obamuyide and Andreas Vlachos. 2018. Zero-shot Relation Classification as Textual Entailment. In Proceedings of the First Workshop on Fact Extraction and VERification (FEVER), pages 72–78, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Zero-shot Relation Classification as Textual Entailment (Obamuyide & Vlachos, EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/W18-5511.pdf
- Data
- MultiNLI