Logic-guided Semantic Representation Learning for Zero-Shot Relation Classification

Juan Li, Ruoxu Wang, Ningyu Zhang, Wen Zhang, Fan Yang, Huajun Chen

Abstract
Relation classification aims to extract semantic relations between entity pairs from the sentences. However, most existing methods can only identify seen relation classes that occurred during training. To recognize unseen relations at test time, we explore the problem of zero-shot relation classification. Previous work regards the problem as reading comprehension or textual entailment, which have to rely on artificial descriptive information to improve the understandability of relation types. Thus, rich semantic knowledge of the relation labels is ignored. In this paper, we propose a novel logic-guided semantic representation learning model for zero-shot relation classification. Our approach builds connections between seen and unseen relations via implicit and explicit semantic representations with knowledge graph embeddings and logic rules. Extensive experimental results demonstrate that our method can generalize to unseen relation types and achieve promising improvements.
Anthology ID:
2020.coling-main.265
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Editors:
Donia Scott, Nuria Bel, Chengqing Zong
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
2967–2978
Language:
URL:
https://aclanthology.org/2020.coling-main.265
DOI:
10.18653/v1/2020.coling-main.265
Bibkey:
Cite (ACL):
Juan Li, Ruoxu Wang, Ningyu Zhang, Wen Zhang, Fan Yang, and Huajun Chen. 2020. Logic-guided Semantic Representation Learning for Zero-Shot Relation Classification. In Proceedings of the 28th International Conference on Computational Linguistics, pages 2967–2978, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
Logic-guided Semantic Representation Learning for Zero-Shot Relation Classification (Li et al., COLING 2020)
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