{"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/slurp-a-spoken-language-understanding-1","title":"SLURP: A Spoken Language Understanding Resource Package","arxiv_id":"2011.13205","date":"2020-11-26","proceeding":"EMNLP 2020 11","authors":["Emanuele Bastianelli","Andrea Vanzo","Pawel Swietojanski","Verena Rieser"],"abstract":"Spoken Language Understanding infers semantic meaning directly from audio data, and thus promises to reduce error propagation and misunderstandings in end-user applications. However, publicly available SLU resources are limited. In this paper, we release SLURP, a new SLU package containing the following: (1) A new challenging dataset in English spanning 18 domains, which is substantially bigger and linguistically more diverse than existing datasets; (2) Competitive baselines based on state-of-the-art NLU and ASR systems; (3) A new transparent metric for entity labelling which enables a detailed error analysis for identifying potential areas of improvement. SLURP is available at https: //github.com/pswietojanski/slurp.","url_abs":"https://arxiv.org/abs/2011.13205v1","url_pdf":"https://arxiv.org/pdf/2011.13205v1.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":"slurp-a-spoken-language-understanding-1","repo_url":"https://github.com/pswietojanski/slurp","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"intent-classification","task_name":"Intent Classification"},{"task_slug":"slot-filling","task_name":"Slot Filling"},{"task_slug":"spoken-language-understanding","task_name":"Spoken Language Understanding"}],"methods":[],"datasets_introduced":[{"slug":"slurp","name":"SLURP","full_name":"Spoken Language Understanding Resource Package"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/intent-classification-on-slurp","task":"Intent Classification","dataset":"SLURP","model":"Multi-SLURP","rank_in_archive_order":3,"of":5,"metrics":{"Accuracy (%)":"78.33"},"uses_additional_data":true},{"leaderboard":"/sota/slot-filling-on-slurp","task":"Slot Filling","dataset":"SLURP","model":"Multi-SLURP","rank_in_archive_order":3,"of":5,"metrics":{"F1":"0.642"},"uses_additional_data":true}],"syntology":{"syntology_url":"https://syntology.ai/paper/2011.13205","atlas_url":"https://app.syntology.ai/?focus=2011.13205","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}