{"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/semantic-role-labeling-as-dependency-parsing","title":"Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures Inside Arguments","arxiv_id":"2110.06865","date":"2021-10-13","proceeding":"COLING 2022 10","authors":["Yu Zhang","Qingrong Xia","Shilin Zhou","Yong Jiang","Guohong Fu","Min Zhang"],"abstract":"Semantic role labeling (SRL) is a fundamental yet challenging task in the NLP community. Recent works of SRL mainly fall into two lines: 1) BIO-based; 2) span-based. Despite ubiquity, they share some intrinsic drawbacks of not considering internal argument structures, potentially hindering the model's expressiveness. The key challenge is arguments are flat structures, and there are no determined subtree realizations for words inside arguments. To remedy this, in this paper, we propose to regard flat argument spans as latent subtrees, accordingly reducing SRL to a tree parsing task. In particular, we equip our formulation with a novel span-constrained TreeCRF to make tree structures span-aware and further extend it to the second-order case. We conduct extensive experiments on CoNLL05 and CoNLL12 benchmarks. Results reveal that our methods perform favorably better than all previous syntax-agnostic works, achieving new state-of-the-art under both end-to-end and w/ gold predicates settings.","url_abs":"https://arxiv.org/abs/2110.06865v2","url_pdf":"https://arxiv.org/pdf/2110.06865v2.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":"semantic-role-labeling-as-dependency-parsing","repo_url":"https://github.com/yzhangcs/crfsrl","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"dependency-parsing","task_name":"Dependency Parsing"},{"task_slug":"semantic-role-labeling","task_name":"Semantic Role Labeling"},{"task_slug":"semantic-role-labeling-predicted-predicates","task_name":"Semantic Role Labeling (predicted predicates)"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/semantic-role-labeling-on-conll-2005","task":"Semantic Role Labeling","dataset":"CoNLL 2005","model":"CRF2o + RoBERTa","rank_in_archive_order":3,"of":15,"metrics":{"F1":"89.54"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-role-labeling-on-conll-2005","task":"Semantic Role Labeling","dataset":"CoNLL 2005","model":"CRF2o + BERT","rank_in_archive_order":5,"of":15,"metrics":{"F1":"89.03"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-role-labeling-on-conll-2005","task":"Semantic Role Labeling","dataset":"CoNLL 2005","model":"CRF2o","rank_in_archive_order":8,"of":15,"metrics":{"F1":"87.87"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-role-labeling-on-ontonotes","task":"Semantic Role Labeling","dataset":"OntoNotes","model":"CRF2o + RoBERTa","rank_in_archive_order":2,"of":17,"metrics":{"F1":"88.32"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-role-labeling-on-ontonotes","task":"Semantic Role Labeling","dataset":"OntoNotes","model":"CRF2o + BERT","rank_in_archive_order":6,"of":17,"metrics":{"F1":"87.66"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-role-labeling-on-ontonotes","task":"Semantic Role Labeling","dataset":"OntoNotes","model":"CRF2o","rank_in_archive_order":14,"of":17,"metrics":{"F1":"83.66"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-role-labeling-predicted-predicates-1","task":"Semantic Role Labeling (predicted predicates)","dataset":"CoNLL 2012","model":"CRF2O + BERT","rank_in_archive_order":2,"of":7,"metrics":{"F1":"85.45"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2110.06865","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.06865"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+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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