{"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/a-simple-and-accurate-syntax-agnostic-neural","title":"A Simple and Accurate Syntax-Agnostic Neural Model for Dependency-based Semantic Role Labeling","arxiv_id":"1701.02593","date":"2017-01-10","proceeding":"CONLL 2017 8","authors":["Diego Marcheggiani","Anton Frolov","Ivan Titov"],"abstract":"We introduce a simple and accurate neural model for dependency-based semantic\nrole labeling. Our model predicts predicate-argument dependencies relying on\nstates of a bidirectional LSTM encoder. The semantic role labeler achieves\ncompetitive performance on English, even without any kind of syntactic\ninformation and only using local inference. However, when automatically\npredicted part-of-speech tags are provided as input, it substantially\noutperforms all previous local models and approaches the best reported results\non the English CoNLL-2009 dataset. We also consider Chinese, Czech and Spanish\nwhere our approach also achieves competitive results. Syntactic parsers are\nunreliable on out-of-domain data, so standard (i.e., syntactically-informed)\nSRL models are hindered when tested in this setting. Our syntax-agnostic model\nappears more robust, resulting in the best reported results on standard\nout-of-domain test sets.","url_abs":"http://arxiv.org/abs/1701.02593v2","url_pdf":"http://arxiv.org/pdf/1701.02593v2.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":"a-simple-and-accurate-syntax-agnostic-neural","repo_url":"https://github.com/diegma/neural-dep-srl","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"a-simple-and-accurate-syntax-agnostic-neural","repo_url":"https://github.com/jungokasai/stagging_srl","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"semantic-role-labeling","task_name":"Semantic Role Labeling"}],"methods":[{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1701.02593","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}