DocAMR: Multi-Sentence AMR Representation and Evaluation
Abstract
Despite extensive research on parsing of English sentences into Abstraction 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.
Cite
@article{arxiv.2112.08513,
title = {DocAMR: Multi-Sentence AMR Representation and Evaluation},
author = {Tahira Naseem and Austin Blodgett and Sadhana Kumaravel and Tim O'Gorman and Young-Suk Lee and Jeffrey Flanigan and Ramón Fernandez Astudillo and Radu Florian and Salim Roukos and Nathan Schneider},
journal= {arXiv preprint arXiv:2112.08513},
year = {2022}
}