{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/amr-similarity-metrics-from-principles","title":"AMR Similarity Metrics from Principles","arxiv_id":"2001.10929","date":"2020-01-29","proceeding":null,"authors":["Juri Opitz","Letitia Parcalabescu","Anette Frank"],"abstract":"Different metrics have been proposed to compare Abstract Meaning Representation (AMR) graphs. The canonical Smatch metric (Cai and Knight, 2013) aligns the variables of two graphs and assesses triple matches. The recent SemBleu metric (Song and Gildea, 2019) is based on the machine-translation metric Bleu (Papineni et al., 2002) and increases computational efficiency by ablating the variable-alignment. In this paper, i) we establish criteria that enable researchers to perform a principled assessment of metrics comparing meaning representations like AMR; ii) we undertake a thorough analysis of Smatch and SemBleu where we show that the latter exhibits some undesirable properties. For example, it does not conform to the identity of indiscernibles rule and introduces biases that are hard to control; iii) we propose a novel metric S$^2$match that is more benevolent to only very slight meaning deviations and targets the fulfilment of all established criteria. We assess its suitability and show its advantages over Smatch and SemBleu.","url_abs":"https://arxiv.org/abs/2001.10929v2","url_pdf":"https://arxiv.org/pdf/2001.10929v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"amr-similarity-metrics-from-principles","repo_url":"https://github.com/Heidelberg-NLP/amr-metric-suite","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"amr-similarity-metrics-from-principles","repo_url":"https://github.com/fliegenpilz357/amr-metric-suite","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"amr-similarity-metrics-from-principles","repo_url":"https://github.com/flipz357/amr-metric-suite","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"abstract-meaning-representation","task_name":"Abstract Meaning Representation"},{"task_slug":"computational-efficiency","task_name":"Computational Efficiency"},{"task_slug":"graph-matching","task_name":"Graph Matching"},{"task_slug":"machine-translation","task_name":"Machine Translation"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/graph-matching-on-rare","task":"Graph Matching","dataset":"RARE","model":"S2match","rank_in_archive_order":4,"of":5,"metrics":{"Spearman Correlation":"94.11"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2001.10929","atlas_url":"https://app.syntology.ai/?focus=2001.10929","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}