@inproceedings{naseem-etal-2022-docamr,
title = "{D}oc{AMR}: Multi-Sentence {AMR} Representation and Evaluation",
author = "Naseem, Tahira and
Blodgett, Austin and
Kumaravel, Sadhana and
O{'}Gorman, Tim and
Lee, Young-Suk and
Flanigan, Jeffrey and
Astudillo, Ram{\'o}n and
Florian, Radu and
Roukos, Salim and
Schneider, Nathan",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.256",
doi = "10.18653/v1/2022.naacl-main.256",
pages = "3496--3505",
abstract = "Despite extensive research on parsing of English sentences into Abstract Meaning Representation (AMR) graphs, which are compared to gold graphs via the Smatch metric, full-document parsing into a unified graph representation lacks well-defined representation and evaluation. Taking advantage of a super-sentential level of coreference annotation from previous work, we introduce a simple algorithm for deriving a unified graph representation, avoiding the pitfalls of information loss from over-merging and lack of coherence from under merging. Next, we describe improvements to the Smatch metric to make it tractable for comparing document-level graphs and use it to re-evaluate the best published document-level AMR parser. We also present a pipeline approach combining the top-performing AMR parser and coreference resolution systems, providing a strong baseline for future research.",
}
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<abstract>Despite extensive research on parsing of English sentences into Abstract Meaning Representation (AMR) graphs, which are compared to gold graphs via the Smatch metric, full-document parsing into a unified graph representation lacks well-defined representation and evaluation. Taking advantage of a super-sentential level of coreference annotation from previous work, we introduce a simple algorithm for deriving a unified graph representation, avoiding the pitfalls of information loss from over-merging and lack of coherence from under merging. Next, we describe improvements to the Smatch metric to make it tractable for comparing document-level graphs and use it to re-evaluate the best published document-level AMR parser. We also present a pipeline approach combining the top-performing AMR parser and coreference resolution systems, providing a strong baseline for future research.</abstract>
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%0 Conference Proceedings
%T DocAMR: Multi-Sentence AMR Representation and Evaluation
%A Naseem, Tahira
%A Blodgett, Austin
%A Kumaravel, Sadhana
%A O’Gorman, Tim
%A Lee, Young-Suk
%A Flanigan, Jeffrey
%A Astudillo, Ramón
%A Florian, Radu
%A Roukos, Salim
%A Schneider, Nathan
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F naseem-etal-2022-docamr
%X Despite extensive research on parsing of English sentences into Abstract Meaning Representation (AMR) graphs, which are compared to gold graphs via the Smatch metric, full-document parsing into a unified graph representation lacks well-defined representation and evaluation. Taking advantage of a super-sentential level of coreference annotation from previous work, we introduce a simple algorithm for deriving a unified graph representation, avoiding the pitfalls of information loss from over-merging and lack of coherence from under merging. Next, we describe improvements to the Smatch metric to make it tractable for comparing document-level graphs and use it to re-evaluate the best published document-level AMR parser. We also present a pipeline approach combining the top-performing AMR parser and coreference resolution systems, providing a strong baseline for future research.
%R 10.18653/v1/2022.naacl-main.256
%U https://aclanthology.org/2022.naacl-main.256
%U https://doi.org/10.18653/v1/2022.naacl-main.256
%P 3496-3505
Markdown (Informal)
[DocAMR: Multi-Sentence AMR Representation and Evaluation](https://aclanthology.org/2022.naacl-main.256) (Naseem et al., NAACL 2022)
ACL
- Tahira Naseem, Austin Blodgett, Sadhana Kumaravel, Tim O’Gorman, Young-Suk Lee, Jeffrey Flanigan, Ramón Astudillo, Radu Florian, Salim Roukos, and Nathan Schneider. 2022. DocAMR: Multi-Sentence AMR Representation and Evaluation. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 3496–3505, Seattle, United States. Association for Computational Linguistics.