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    <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.</abstract>
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%0 Conference Proceedings
%T AMR Parsing is Far from Solved: GrAPES, the Granular AMR Parsing Evaluation Suite
%A Groschwitz, Jonas
%A Cohen, Shay
%A Donatelli, Lucia
%A Fowlie, Meaghan
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F groschwitz-etal-2023-amr
%X 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.
%R 10.18653/v1/2023.emnlp-main.662
%U https://aclanthology.org/2023.emnlp-main.662
%U https://doi.org/10.18653/v1/2023.emnlp-main.662
%P 10728-10752

Markdown (Informal)

[AMR Parsing is Far from Solved: GrAPES, the Granular AMR Parsing Evaluation Suite](https://aclanthology.org/2023.emnlp-main.662) (Groschwitz et al., EMNLP 2023)

• AMR Parsing is Far from Solved: GrAPES, the Granular AMR Parsing Evaluation Suite (Groschwitz et al., EMNLP 2023)

ACL

• Jonas Groschwitz, Shay Cohen, Lucia Donatelli, and Meaghan Fowlie. 2023. AMR Parsing is Far from Solved: GrAPES, the Granular AMR Parsing Evaluation Suite. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 10728–10752, Singapore. Association for Computational Linguistics.