{"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/dynamic-oracles-for-top-down-and-in-order","title":"Dynamic Oracles for Top-Down and In-Order Shift-Reduce Constituent Parsing","arxiv_id":"1810.10882","date":"2018-10-25","proceeding":null,"authors":["Daniel Fernández-González","Carlos Gómez-Rodríguez"],"abstract":"We introduce novel dynamic oracles for training two of the most accurate\nknown shift-reduce algorithms for constituent parsing: the top-down and\nin-order transition-based parsers. In both cases, the dynamic oracles manage to\nnotably increase their accuracy, in comparison to that obtained by performing\nclassic static training. In addition, by improving the performance of the\nstate-of-the-art in-order shift-reduce parser, we achieve the best accuracy to\ndate (92.0 F1) obtained by a fully-supervised single-model greedy shift-reduce\nconstituent parser on the WSJ benchmark.","url_abs":"http://arxiv.org/abs/1810.10882v1","url_pdf":"http://arxiv.org/pdf/1810.10882v1.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":"dynamic-oracles-for-top-down-and-in-order","repo_url":"https://github.com/danifg/Dynamic-InOrderParser","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1810.10882","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}