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
- Editors:
- Chengqing Zong, Fei Xia, Wenjie Li, Roberto Navigli
- 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
- Cite (ACL):
- Qiankun Fu, Linfeng Song, Wenyu Du, and Yue Zhang. 2021. End-to-End AMR Coreference Resolution. In 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), pages 4204–4214, Online. Association for Computational Linguistics.
- Cite (Informal):
- End-to-End AMR Coreference Resolution (Fu et al., ACL-IJCNLP 2021)
- PDF:
- https://preview.aclanthology.org/ingest-bitext-workshop/2021.acl-long.324.pdf
- Code
- sean-blank/amrcoref