{"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/globally-normalized-transition-based-neural","title":"Globally Normalized Transition-Based Neural Networks","arxiv_id":"1603.06042","date":"2016-03-19","proceeding":"ACL 2016 8","authors":["Daniel Andor","Chris Alberti","David Weiss","Aliaksei Severyn","Alessandro Presta","Kuzman Ganchev","Slav Petrov","Michael Collins"],"abstract":"We introduce a globally normalized transition-based neural network model that\nachieves state-of-the-art part-of-speech tagging, dependency parsing and\nsentence compression results. Our model is a simple feed-forward neural network\nthat operates on a task-specific transition system, yet achieves comparable or\nbetter accuracies than recurrent models. We discuss the importance of global as\nopposed to local normalization: a key insight is that the label bias problem\nimplies that globally normalized models can be strictly more expressive than\nlocally normalized models.","url_abs":"http://arxiv.org/abs/1603.06042v2","url_pdf":"http://arxiv.org/pdf/1603.06042v2.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":"globally-normalized-transition-based-neural","repo_url":"https://github.com/tensorflow/models/tree/master/research/syntaxnet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"dependency-parsing","task_name":"Dependency Parsing"},{"task_slug":"part-of-speech-tagging","task_name":"Part-Of-Speech Tagging"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"sentence-compression","task_name":"Sentence Compression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/dependency-parsing-on-penn-treebank","task":"Dependency Parsing","dataset":"Penn Treebank","model":"Andor et al.","rank_in_archive_order":17,"of":22,"metrics":{"LAS":"92.79","POS":"97.44","UAS":"94.61"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1603.06042","atlas_url":"https://app.syntology.ai/?focus=1603.06042","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}