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

Autor:	Groschwitz, Jonas, Cohen, Shay B., Donatelli, Lucia, Fowlie, Meaghan
Rok vydání:	2023
Předmět:	Computer Science - Computation and Language J.5
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted at EMNLP 2023. For the associated GitHub repository, see https://github.com/jgroschwitz/GrAPES
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2312.03480 Zobrazit plný text záznamu View this record from Arxiv