{"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/slot-gated-modeling-for-joint-slot-filling","title":"Slot-Gated Modeling for Joint Slot Filling and Intent Prediction","arxiv_id":null,"date":"2018-06-01","proceeding":"NAACL 2018 6","authors":["Chih-Wen Goo","Guang Gao","Yun-Kai Hsu","Chih-Li Huo","Tsung-Chieh Chen","Keng-Wei Hsu","Yun-Nung Chen"],"abstract":"Attention-based recurrent neural network models for joint intent detection and slot filling have achieved the state-of-the-art performance, while they have independent attention weights. Considering that slot and intent have the strong relationship, this paper proposes a slot gate that focuses on learning the relationship between intent and slot attention vectors in order to obtain better semantic frame results by the global optimization. The experiments show that our proposed model significantly improves sentence-level semantic frame accuracy with 4.2{\\%} and 1.9{\\%} relative improvement compared to the attentional model on benchmark ATIS and Snips datasets respectively","url_abs":"https://aclanthology.org/N18-2118","url_pdf":"https://aclanthology.org/N18-2118.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":"slot-gated-modeling-for-joint-slot-filling","repo_url":"https://github.com/MiuLab/SlotGated-SLU","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"slot-gated-modeling-for-joint-slot-filling","repo_url":"https://github.com/bo-ke/cybo/tree/master/tutorials/slu/slot_gated","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"intent-detection","task_name":"Intent Detection"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"slot-filling","task_name":"Slot Filling"},{"task_slug":"spoken-dialogue-systems","task_name":"Spoken Dialogue Systems"},{"task_slug":"spoken-language-understanding","task_name":"Spoken Language Understanding"},{"task_slug":"global-optimization","task_name":"global-optimization"},{"task_slug":"slot-filling-1","task_name":"slot-filling"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/intent-detection-on-atis","task":"Intent Detection","dataset":"ATIS","model":"Slot-Gated BLSTM with Attension","rank_in_archive_order":15,"of":16,"metrics":{"Accuracy":"93.6"},"uses_additional_data":false},{"leaderboard":"/sota/intent-detection-on-snips","task":"Intent Detection","dataset":"SNIPS","model":"Slot-Gated BLSTM with Attension","rank_in_archive_order":10,"of":10,"metrics":{"Accuracy":"97.00"},"uses_additional_data":false},{"leaderboard":"/sota/slot-filling-on-atis","task":"Slot Filling","dataset":"ATIS","model":"Slot-Gated BLSTM with Attension","rank_in_archive_order":13,"of":14,"metrics":{"F1":"0.948"},"uses_additional_data":false},{"leaderboard":"/sota/slot-filling-on-snips","task":"Slot Filling","dataset":"SNIPS","model":"Slot-Gated BLSTM with Attension","rank_in_archive_order":8,"of":10,"metrics":{"F1":"88.8"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}