{"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/semantic-role-labeling-for-learner-chinese","title":"Semantic Role Labeling for Learner Chinese: the Importance of Syntactic Parsing and L2-L1 Parallel Data","arxiv_id":"1808.09409","date":"2018-08-28","proceeding":"EMNLP 2018 10","authors":["Zi Lin","Yuguang Duan","Yuan-Yuan Zhao","Weiwei Sun","Xiaojun Wan"],"abstract":"This paper studies semantic parsing for interlanguage (L2), taking semantic\nrole labeling (SRL) as a case task and learner Chinese as a case language. We\nfirst manually annotate the semantic roles for a set of learner texts to derive\na gold standard for automatic SRL. Based on the new data, we then evaluate\nthree off-the-shelf SRL systems, i.e., the PCFGLA-parser-based,\nneural-parser-based and neural-syntax-agnostic systems, to gauge how successful\nSRL for learner Chinese can be. We find two non-obvious facts: 1) the\nL1-sentence-trained systems performs rather badly on the L2 data; 2) the\nperformance drop from the L1 data to the L2 data of the two parser-based\nsystems is much smaller, indicating the importance of syntactic parsing in SRL\nfor interlanguages. Finally, the paper introduces a new agreement-based model\nto explore the semantic coherency information in the large-scale L2-L1 parallel\ndata. We then show such information is very effective to enhance SRL for\nlearner texts. Our model achieves an F-score of 72.06, which is a 2.02 point\nimprovement over the best baseline.","url_abs":"http://arxiv.org/abs/1808.09409v2","url_pdf":"http://arxiv.org/pdf/1808.09409v2.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":"semantic-role-labeling-for-learner-chinese","repo_url":"https://github.com/pkucoli/srl4il","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"semantic-parsing","task_name":"Semantic Parsing"},{"task_slug":"semantic-role-labeling","task_name":"Semantic Role Labeling"},{"task_slug":"sentence","task_name":"Sentence"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}