{"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/jointly-predicting-predicates-and-arguments","title":"Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling","arxiv_id":"1805.04787","date":"2018-05-12","proceeding":"ACL 2018 7","authors":["Luheng He","Kenton Lee","Omer Levy","Luke Zettlemoyer"],"abstract":"Recent BIO-tagging-based neural semantic role labeling models are very high\nperforming, but assume gold predicates as part of the input and cannot\nincorporate span-level features. We propose an end-to-end approach for jointly\npredicting all predicates, arguments spans, and the relations between them. The\nmodel makes independent decisions about what relationship, if any, holds\nbetween every possible word-span pair, and learns contextualized span\nrepresentations that provide rich, shared input features for each decision.\nExperiments demonstrate that this approach sets a new state of the art on\nPropBank SRL without gold predicates.","url_abs":"http://arxiv.org/abs/1805.04787v2","url_pdf":"http://arxiv.org/pdf/1805.04787v2.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":"jointly-predicting-predicates-and-arguments","repo_url":"https://github.com/luheng/lsgn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"semantic-role-labeling","task_name":"Semantic Role Labeling"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/semantic-role-labeling-on-conll-2005","task":"Semantic Role Labeling","dataset":"CoNLL 2005","model":"He et al. (2018) + ELMo","rank_in_archive_order":13,"of":15,"metrics":{"F1":"86.0"},"uses_additional_data":true},{"leaderboard":"/sota/semantic-role-labeling-on-conll-2005","task":"Semantic Role Labeling","dataset":"CoNLL 2005","model":"He et al. (2018)","rank_in_archive_order":15,"of":15,"metrics":{"F1":"82.5"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-role-labeling-on-ontonotes","task":"Semantic Role Labeling","dataset":"OntoNotes","model":"He et al.,","rank_in_archive_order":12,"of":17,"metrics":{"F1":"85.5"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-role-labeling-on-ontonotes","task":"Semantic Role Labeling","dataset":"OntoNotes","model":"He et al.","rank_in_archive_order":16,"of":17,"metrics":{"F1":"82.1"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-role-labeling-predicted-predicates","task":"Semantic Role Labeling (predicted predicates)","dataset":"CoNLL 2005","model":"He et al. 2018 + ELMo","rank_in_archive_order":2,"of":5,"metrics":{"F1":"86.0"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-role-labeling-predicted-predicates","task":"Semantic Role Labeling (predicted predicates)","dataset":"CoNLL 2005","model":"He et al. (2018)","rank_in_archive_order":3,"of":5,"metrics":{"F1":"86.0"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-role-labeling-predicted-predicates","task":"Semantic Role Labeling (predicted predicates)","dataset":"CoNLL 2005","model":"He et al. 2018","rank_in_archive_order":5,"of":5,"metrics":{"F1":"82.5"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-role-labeling-predicted-predicates-1","task":"Semantic Role Labeling (predicted predicates)","dataset":"CoNLL 2012","model":"He et al. 2018 + ELMo","rank_in_archive_order":4,"of":7,"metrics":{"F1":"82.9"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-role-labeling-predicted-predicates-1","task":"Semantic Role Labeling (predicted predicates)","dataset":"CoNLL 2012","model":"He et al. 2018","rank_in_archive_order":7,"of":7,"metrics":{"F1":"79.8"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1805.04787","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}