AMR Parsing is Far from Solved GrAPES, the Granular AMR Parsing Evaluation Suite

Open Access
Authors
Publication date 2023
Host editors
  • H. Bouamor
  • J. Pino
  • K. Bali
Book title The 2023 Conference on Empirical Methods in Natural Language Processing
Book subtitle EMNLP 2023 : Proceedings of the Conference : December 6-10, 2023
ISBN (electronic)
  • 9798891760608
Event 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023
Pages (from-to) 10728-10752
Number of pages 25
Publisher Stroudsburg, PA: Association for Computational Linguistics
Organisations
  • Interfacultary Research - Institute for Logic, Language and Computation (ILLC)
Abstract

We present the Granular AMR Parsing Evaluation Suite (GrAPES), a challenge set for Abstract Meaning Representation (AMR) parsing with accompanying evaluation metrics. AMR parsers now obtain high scores on the standard AMR evaluation metric Smatch, close to or even above reported inter-annotator agreement. But that does not mean that AMR parsing is solved; in fact, human evaluation in previous work indicates that current parsers still quite frequently make errors on node labels or graph structure that substantially distort sentence meaning. Here, we provide an evaluation suite that tests AMR parsers on a range of phenomena of practical, technical, and linguistic interest. Our 36 categories range from seen and unseen labels, to structural generalization, to coreference. GrAPES reveals in depth the abilities and shortcomings of current AMR parsers.

Document type Conference contribution
Note With supplementary video
Language English
Published at
Other links
Downloads
2023.emnlp-main.662 (Final published version)
Supplementary materials
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