End-to-End AMR Coreference Resolution

Qiankun Fu, Linfeng Song, Wenyu Du, Yue Zhang


Abstract
Although parsing to Abstract Meaning Representation (AMR) has become very popular and AMR has been shown effective on the many sentence-level downstream tasks, little work has studied how to generate AMRs that can represent multi-sentence information. We introduce the first end-to-end AMR coreference resolution model in order to build multi-sentence AMRs. Compared with the previous pipeline and rule-based approaches, our model alleviates error propagation and it is more robust for both in-domain and out-domain situations. Besides, the document-level AMRs obtained by our model can significantly improve over the AMRs generated by a rule-based method (Liu et al., 2015) on text summarization.
Anthology ID:
2021.acl-long.324
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4204–4214
Language:
URL:
https://aclanthology.org/2021.acl-long.324
DOI:
10.18653/v1/2021.acl-long.324
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-long.324.pdf