{"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/exploiting-rich-syntactic-information-for","title":"Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model","arxiv_id":"1808.07624","date":"2018-08-23","proceeding":"EMNLP 2018 10","authors":["Kun Xu","Lingfei Wu","Zhiguo Wang","Mo Yu","Li-Wei Chen","Vadim Sheinin"],"abstract":"Existing neural semantic parsers mainly utilize a sequence encoder, i.e., a\nsequential LSTM, to extract word order features while neglecting other valuable\nsyntactic information such as dependency graph or constituent trees. In this\npaper, we first propose to use the \\textit{syntactic graph} to represent three\ntypes of syntactic information, i.e., word order, dependency and constituency\nfeatures. We further employ a graph-to-sequence model to encode the syntactic\ngraph and decode a logical form. Experimental results on benchmark datasets\nshow that our model is comparable to the state-of-the-art on Jobs640, ATIS and\nGeo880. Experimental results on adversarial examples demonstrate the robustness\nof the model is also improved by encoding more syntactic information.","url_abs":"http://arxiv.org/abs/1808.07624v1","url_pdf":"http://arxiv.org/pdf/1808.07624v1.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":"exploiting-rich-syntactic-information-for","repo_url":"https://github.com/IBM/Text-to-LogicForm","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"graph-to-sequence","task_name":"Graph-to-Sequence"},{"task_slug":"semantic-parsing","task_name":"Semantic Parsing"}],"methods":[{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1808.07624","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1808.07624"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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