{"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-representation-learning-framework-for-multi","title":"A Representation Learning Framework for Multi-Source Transfer Parsing","arxiv_id":null,"date":"2016-03-05","proceeding":null,"authors":["Jiang Guo","Wanxiang Che","David Yarowsky","Haifeng Wang","Ting Liu"],"abstract":"Cross-lingual model transfer has been a promising approach for inducing dependency parsers for low-resource languages where annotated treebanks are not available. The major obstacles for the model transfer approach are two-fold: 1. Lexical features are not directly transferable across languages; 2. Target language-specific syntactic structures are difficult to be recovered. To address these two challenges, we present a novel representation learning framework for multi-source transfer parsing. Our framework allows multi-source transfer parsing using full lexical features straightforwardly. By evaluating on the Google universal dependency treebanks (v2.0), our best models yield an absolute improvement of 6.53% in averaged labeled attachment score, as compared with delexicalized multi-source transfer models. We also significantly outperform the state-of-the-art transfer system proposed most recently.","url_abs":"https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/12236","url_pdf":"https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/12236/12016","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":[],"tasks":[{"task_slug":"cross-lingual-zero-shot-dependency-parsing","task_name":"Cross-lingual zero-shot dependency parsing"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/cross-lingual-zero-shot-dependency-parsing-on","task":"Cross-lingual zero-shot dependency parsing","dataset":"Universal Dependency Treebank","model":"MULTI-PROJ","rank_in_archive_order":3,"of":3,"metrics":{"LAS":"69.3","UAS":"76.4"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}