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
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.
BibTeX
@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"
}