@inproceedings{fu-etal-2021-end,
title = "End-to-End {AMR} Coreference Resolution",
author = "Fu, Qiankun and
Song, Linfeng and
Du, Wenyu and
Zhang, Yue",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "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 = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.324",
doi = "10.18653/v1/2021.acl-long.324",
pages = "4204--4214",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T End-to-End AMR Coreference Resolution
%A Fu, Qiankun
%A Song, Linfeng
%A Du, Wenyu
%A Zhang, Yue
%Y Zong, Chengqing
%Y Xia, Fei
%Y Li, Wenjie
%Y Navigli, Roberto
%S 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)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F fu-etal-2021-end
%X 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.
%R 10.18653/v1/2021.acl-long.324
%U https://aclanthology.org/2021.acl-long.324
%U https://doi.org/10.18653/v1/2021.acl-long.324
%P 4204-4214
Markdown (Informal)
[End-to-End AMR Coreference Resolution](https://aclanthology.org/2021.acl-long.324) (Fu et al., ACL-IJCNLP 2021)
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.