Abstract
External syntactic and semantic information has been largely ignored by existing neural coreference resolution models. In this paper, we present a heterogeneous graph-based model to incorporate syntactic and semantic structures of sentences. The proposed graph contains a syntactic sub-graph where tokens are connected based on a dependency tree, and a semantic sub-graph that contains arguments and predicates as nodes and semantic role labels as edges. By applying a graph attention network, we can obtain syntactically and semantically augmented word representation, which can be integrated using an attentive integration layer and gating mechanism. Experiments on the OntoNotes 5.0 benchmark show the effectiveness of our proposed model.- Anthology ID:
- 2021.naacl-main.125
- Volume:
- Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Month:
- June
- Year:
- 2021
- Address:
- Online
- Editors:
- Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, Yichao Zhou
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1584–1591
- Language:
- URL:
- https://aclanthology.org/2021.naacl-main.125
- DOI:
- 10.18653/v1/2021.naacl-main.125
- Cite (ACL):
- Fan Jiang and Trevor Cohn. 2021. Incorporating Syntax and Semantics in Coreference Resolution with Heterogeneous Graph Attention Network. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1584–1591, Online. Association for Computational Linguistics.
- Cite (Informal):
- Incorporating Syntax and Semantics in Coreference Resolution with Heterogeneous Graph Attention Network (Jiang & Cohn, NAACL 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2021.naacl-main.125.pdf
- Code
- fantabulous-j/coref-hgat