AMR Parsing is Far from Solved: GrAPES, the Granular AMR Parsing Evaluation Suite (2312.03480v1)

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.

• The paper introduces GrAPES, an evaluation suite that uses 36 targeted metrics to reveal specific weaknesses in current AMR parsers.
• It demonstrates that high overall scores can hide significant errors in node labeling and graph structure that distort sentence meaning.
• The findings underline persistent challenges in AMR parsing despite recent advances, guiding researchers to focus on refining parser robustness.

Evaluation Challenges in AMR Parsing

Introduction to Abstract Meaning Representation (AMR) Parsing

Abstract Meaning Representation (AMR) parsing involves the process of converting a natural language sentence into a semantic representation, which is a graph-like structure that captures its meaning. Although recent AMR parsers have shown high performance using standard evaluation metrics like Smatch, close examination reveals that these metrics may not fully capture the deficiencies in parsing performance. While high scores suggest that AMR parsing may be close to human-level performance, in-depth analysis discloses frequent errors in node labeling and graph structure that significantly alter the intended meaning of the sentences.

Uncovering Parsing Shortcomings: GrAPES

A new evaluation suite, the Granular AMR Parsing Evaluation Suite (GrAPES), has been developed to assess the abilities and weaknesses of current AMR parsers with greater precision. GrAPES includes 36 categories that test a parser's performance on various phenomena. These range from linguistic issues like coreference and ellipsis to the handling of rare words and unseen entities. The GrAPES framework not only identifies specific areas where parsers underperform but also serves as a fine-grained tool that highlights their differences. The metrics in GrAPES aim to offer accurate evaluations of specific parsing phenomena, allowing for more detailed and interpretable results than a single score like Smatch.

Methodology and Goals

Through careful annotation and targeted metrics, GrAPES allows for a high-quality, detailed analysis of AMR parsing performance. It focuses on evaluating phenomena individually, with metrics specifically designed for the category under investigation. The suite uses sanity checks and prerequisites to ensure that performance is not penalized for errors unrelated to the specific phenomena being tested. GrAPES provides a three-fold benefit: quantitative insight into distinct parsing challenges, comparative analysis of parsers, and guidance for developers to improve their systems in key areas.

Insights and Contributions

The paper using GrAPES revealed several insights. It confirmed that current AMR parsers struggle with phenomena related to data scarcity, ambiguity resolution, and structural generalization. While the most recent parser, AMRBart, demonstrated improvements over older systems, none of the evaluated parsers were free from significant deficiencies. The research concludes that despite the measurable advances, there are still crucial challenges to overcome in AMR parsing. The GrAPES suite itself stands as a comprehensive resource for the computational linguistics community, offering granular evaluation, new annotated data, and tools aiding parser development.