DocAMR: Multi-Sentence AMR Representation and Evaluation

Tahira Naseem, Austin Blodgett, Sadhana Kumaravel, Tim O’Gorman, Young-Suk Lee, Jeffrey Flanigan, Ramón Astudillo, Radu Florian, Salim Roukos, Nathan Schneider


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.
Anthology ID:
2022.naacl-main.256
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3496–3505
Language:
URL:
https://aclanthology.org/2022.naacl-main.256
DOI:
10.18653/v1/2022.naacl-main.256
Bibkey:
Cite (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.
Cite (Informal):
DocAMR: Multi-Sentence AMR Representation and Evaluation (Naseem et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.256.pdf
Code
 ibm/docamr