{"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/broad-coverage-semantic-parsing-as","title":"Broad-Coverage Semantic Parsing as Transduction","arxiv_id":"1909.02607","date":"2019-09-05","proceeding":"IJCNLP 2019 11","authors":["Sheng Zhang","Xutai Ma","Kevin Duh","Benjamin Van Durme"],"abstract":"We unify different broad-coverage semantic parsing tasks under a transduction paradigm, and propose an attention-based neural framework that incrementally builds a meaning representation via a sequence of semantic relations. By leveraging multiple attention mechanisms, the transducer can be effectively trained without relying on a pre-trained aligner. Experiments conducted on three separate broad-coverage semantic parsing tasks -- AMR, SDP and UCCA -- demonstrate that our attention-based neural transducer improves the state of the art on both AMR and UCCA, and is competitive with the state of the art on SDP.","url_abs":"https://arxiv.org/abs/1909.02607v2","url_pdf":"https://arxiv.org/pdf/1909.02607v2.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":[],"tasks":[{"task_slug":"amr-parsing","task_name":"AMR Parsing"},{"task_slug":"semantic-parsing","task_name":"Semantic Parsing"},{"task_slug":"ucca-parsing","task_name":"UCCA Parsing"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/amr-parsing-on-ldc2014t12-1","task":"AMR Parsing","dataset":"LDC2014T12","model":"Broad-Coverage Semantic Parsing as Transduction","rank_in_archive_order":4,"of":12,"metrics":{"F1 Full":"71.3"},"uses_additional_data":false},{"leaderboard":"/sota/amr-parsing-on-ldc2017t10","task":"AMR Parsing","dataset":"LDC2017T10","model":"Zhang et al.","rank_in_archive_order":20,"of":27,"metrics":{"Smatch":"77.0"},"uses_additional_data":false},{"leaderboard":"/sota/ucca-parsing-on-semeval-2019-task-1","task":"UCCA Parsing","dataset":"SemEval 2019 Task 1","model":"Neural Transducer","rank_in_archive_order":2,"of":4,"metrics":{"English-Wiki (open) F1":"76.6"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1909.02607","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}