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We extend the LSTM-based\nsyntactic parser of Dozat and Manning (2017) to train on and generate these\ngraph structures. The resulting system on its own achieves state-of-the-art\nperformance, beating the previous, substantially more complex state-of-the-art\nsystem by 0.6% labeled F1. Adding linguistically richer input representations\npushes the margin even higher, allowing us to beat it by 1.9% labeled F1.","url_abs":"http://arxiv.org/abs/1807.01396v1","url_pdf":"http://arxiv.org/pdf/1807.01396v1.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":"simpler-but-more-accurate-semantic-dependency","repo_url":"https://github.com/tdozat/Parser-v3","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"simpler-but-more-accurate-semantic-dependency","repo_url":"https://gitlab.com/ucdavisnlp/dialog-parsing","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"simpler-but-more-accurate-semantic-dependency","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"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/semantic-dependency-parsing-on-dm","task":"Semantic Dependency Parsing","dataset":"DM","model":"Dozat et al. 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