{"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/second-order-semantic-dependency-parsing-with","title":"Second-Order Semantic Dependency Parsing with End-to-End Neural Networks","arxiv_id":"1906.07880","date":"2019-06-19","proceeding":"ACL 2019 7","authors":["Xinyu Wang","Jingxian Huang","Kewei Tu"],"abstract":"Semantic dependency parsing aims to identify semantic relationships between words in a sentence that form a graph. In this paper, we propose a second-order semantic dependency parser, which takes into consideration not only individual dependency edges but also interactions between pairs of edges. We show that second-order parsing can be approximated using mean field (MF) variational inference or loopy belief propagation (LBP). We can unfold both algorithms as recurrent layers of a neural network and therefore can train the parser in an end-to-end manner. Our experiments show that our approach achieves state-of-the-art performance.","url_abs":"https://arxiv.org/abs/1906.07880v3","url_pdf":"https://arxiv.org/pdf/1906.07880v3.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":"second-order-semantic-dependency-parsing-with","repo_url":"https://github.com/wangxinyu0922/Second_Order_SDP","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"second-order-semantic-dependency-parsing-with","repo_url":"https://github.com/Alibaba-NLP/MultilangStructureKD","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"second-order-semantic-dependency-parsing-with","repo_url":"https://github.com/wangxinyu0922/Second_Order_Parsing","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"second-order-semantic-dependency-parsing-with","repo_url":"https://github.com/yzhangcs/parser","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"dependency-parsing","task_name":"Dependency Parsing"},{"task_slug":"semantic-dependency-parsing","task_name":"Semantic Dependency Parsing"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"variational-inference","task_name":"Variational Inference"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/semantic-dependency-parsing-on-dm","task":"Semantic Dependency Parsing","dataset":"DM","model":"MFVI","rank_in_archive_order":3,"of":4,"metrics":{"In-domain":"94.0","Out-of-domain":"89.7"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-dependency-parsing-on-pas","task":"Semantic Dependency Parsing","dataset":"PAS","model":"MFVI","rank_in_archive_order":3,"of":4,"metrics":{"In-domain":"94.1","Out-of-domain":"91.3"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-dependency-parsing-on-psd","task":"Semantic Dependency Parsing","dataset":"PSD","model":"MFVI","rank_in_archive_order":3,"of":4,"metrics":{"In-domain":"81.4","Out-of-domain":"79.6"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1906.07880","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}