{"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/uni-mis-united-multiple-intent-spoken","title":"Uni-MIS: United Multiple Intent Spoken Language Understanding via Multi-View Intent-Slot Interaction","arxiv_id":null,"date":"2024-03-24","proceeding":"Proceedings of the AAAI Conference on Artificial Intelligence 2024 3","authors":["Shangjian Yin","Peijie Huang","Yuhong Xu"],"abstract":"So far, multi-intent spoken language understanding (SLU) has become a research hotspot in the field of natural language processing (NLP) due to its ability to recognize and extract multiple intents expressed and annotate corresponding sequence slot tags within a single utterance. Previous research has primarily concentrated on the token-level intent-slot interaction to model joint intent detection and slot filling, which resulted in a failure to fully utilize anisotropic intent-guiding information during joint training. In this work, we present a novel architecture by modeling the multi-intent SLU as a multi-view intent-slot interaction. The architecture resolves the kernel bottleneck of unified multi-intent SLU by effectively modeling the intent-slot relations with utterance, chunk, and token-level interaction. We further develop a neural framework, namely Uni-MIS, in which the unified multi-intent SLU is modeled as a three-view intent-slot interaction fusion to better capture the interaction information after special encoding. A chunk-level intent detection decoder is used to sufficiently capture the multi-intent, and an adaptive intent-slot graph network is used to capture the fine-grained intent information to guide final slot filling. We perform extensive experiments on two widely used benchmark datasets for multi-intent SLU, where our model bets on all the current strong baselines, pushing the state-of-the-art performance of unified multi-intent SLU. Additionally, the ChatGPT benchmark that we have developed demonstrates that there is a considerable amount of potential research value in the field of multi-intent SLU.","url_abs":"https://ojs.aaai.org/index.php/AAAI/article/view/29910","url_pdf":"https://ojs.aaai.org/index.php/AAAI/article/view/29910/31590","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":"uni-mis-united-multiple-intent-spoken","repo_url":"https://github.com/SJY8460/Uni-MIS","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"intent-detection","task_name":"Intent Detection"},{"task_slug":"slot-filling","task_name":"Slot Filling"},{"task_slug":"spoken-language-understanding","task_name":"Spoken Language Understanding"},{"task_slug":"slot-filling-1","task_name":"slot-filling"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/intent-detection-on-mixatis","task":"Intent Detection","dataset":"MixATIS","model":"Uni-MIS","rank_in_archive_order":9,"of":15,"metrics":{"Accuracy":"78.5"},"uses_additional_data":false},{"leaderboard":"/sota/intent-detection-on-mixsnips","task":"Intent Detection","dataset":"MixSNIPS","model":"Uni-MIS","rank_in_archive_order":8,"of":16,"metrics":{"Accuracy":"97.2"},"uses_additional_data":false},{"leaderboard":"/sota/slot-filling-on-mixatis","task":"Slot Filling","dataset":"MixATIS","model":"Uni-MIS","rank_in_archive_order":11,"of":15,"metrics":{"Micro F1":"88.3"},"uses_additional_data":false},{"leaderboard":"/sota/slot-filling-on-mixsnips","task":"Slot Filling","dataset":"MixSNIPS","model":"Uni-MIS","rank_in_archive_order":4,"of":16,"metrics":{"Micro F1":"96.4"},"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}