{"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/ensembling-graph-predictions-for-amr-parsing","title":"Ensembling Graph Predictions for AMR Parsing","arxiv_id":"2110.09131","date":"2021-10-18","proceeding":"NeurIPS 2021 12","authors":["Hoang Thanh Lam","Gabriele Picco","Yufang Hou","Young-suk Lee","Lam M. Nguyen","Dzung T. Phan","Vanessa López","Ramon Fernandez Astudillo"],"abstract":"In many machine learning tasks, models are trained to predict structure data such as graphs. For example, in natural language processing, it is very common to parse texts into dependency trees or abstract meaning representation (AMR) graphs. On the other hand, ensemble methods combine predictions from multiple models to create a new one that is more robust and accurate than individual predictions. In the literature, there are many ensembling techniques proposed for classification or regression problems, however, ensemble graph prediction has not been studied thoroughly. In this work, we formalize this problem as mining the largest graph that is the most supported by a collection of graph predictions. As the problem is NP-Hard, we propose an efficient heuristic algorithm to approximate the optimal solution. To validate our approach, we carried out experiments in AMR parsing problems. The experimental results demonstrate that the proposed approach can combine the strength of state-of-the-art AMR parsers to create new predictions that are more accurate than any individual models in five standard benchmark datasets.","url_abs":"https://arxiv.org/abs/2110.09131v2","url_pdf":"https://arxiv.org/pdf/2110.09131v2.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":"ensembling-graph-predictions-for-amr-parsing","repo_url":"https://github.com/ibm/graph_ensemble_learning","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"amr-parsing","task_name":"AMR Parsing"},{"task_slug":"abstract-meaning-representation","task_name":"Abstract Meaning Representation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/amr-parsing-on-bio","task":"AMR Parsing","dataset":"Bio","model":"Graphene Smatch","rank_in_archive_order":3,"of":5,"metrics":{"Smatch":"62.8"},"uses_additional_data":false},{"leaderboard":"/sota/amr-parsing-on-ldc2017t10","task":"AMR Parsing","dataset":"LDC2017T10","model":"Graphene Smatch (IBM)","rank_in_archive_order":2,"of":27,"metrics":{"Smatch":"86.26"},"uses_additional_data":true},{"leaderboard":"/sota/amr-parsing-on-ldc2017t10","task":"AMR Parsing","dataset":"LDC2017T10","model":"Graphene Support (IBM)","rank_in_archive_order":5,"of":27,"metrics":{"Smatch":"85.85"},"uses_additional_data":true},{"leaderboard":"/sota/amr-parsing-on-ldc2020t02","task":"AMR Parsing","dataset":"LDC2020T02","model":"Graphene Smatch (IBM)","rank_in_archive_order":2,"of":13,"metrics":{"Smatch":"84.87"},"uses_additional_data":true},{"leaderboard":"/sota/amr-parsing-on-ldc2020t02","task":"AMR Parsing","dataset":"LDC2020T02","model":"Graphene Support (IBM)","rank_in_archive_order":4,"of":13,"metrics":{"Smatch":"84.41"},"uses_additional_data":true},{"leaderboard":"/sota/amr-parsing-on-new3","task":"AMR Parsing","dataset":"New3","model":"Graphene Smatch","rank_in_archive_order":2,"of":4,"metrics":{"Smatch":"76.32"},"uses_additional_data":false},{"leaderboard":"/sota/amr-parsing-on-the-little-prince","task":"AMR Parsing","dataset":"The Little Prince","model":"Graphene Smatch","rank_in_archive_order":2,"of":4,"metrics":{"Smatch":"79.52"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2110.09131","atlas_url":"https://app.syntology.ai/?focus=2110.09131","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.09131"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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