{"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/a-hybrid-architecture-for-out-of-domain","title":"A Hybrid Architecture for Out of Domain Intent Detection and Intent Discovery","arxiv_id":"2303.04134","date":"2023-03-07","proceeding":null,"authors":["Masoud Akbari","Ali Mohades","M. Hassan Shirali-Shahreza"],"abstract":"Intent Detection is one of the tasks of the Natural Language Understanding (NLU) unit in task-oriented dialogue systems. Out of Scope (OOS) and Out of Domain (OOD) inputs may run these systems into a problem. On the other side, a labeled dataset is needed to train a model for Intent Detection in task-oriented dialogue systems. The creation of a labeled dataset is time-consuming and needs human resources. The purpose of this article is to address mentioned problems. The task of identifying OOD/OOS inputs is named OOD/OOS Intent Detection. Also, discovering new intents and pseudo-labeling of OOD inputs is well known by Intent Discovery. In OOD intent detection part, we make use of a Variational Autoencoder to distinguish between known and unknown intents independent of input data distribution. After that, an unsupervised clustering method is used to discover different unknown intents underlying OOD/OOS inputs. We also apply a non-linear dimensionality reduction on OOD/OOS representations to make distances between representations more meaning full for clustering. Our results show that the proposed model for both OOD/OOS Intent Detection and Intent Discovery achieves great results and passes baselines in English and Persian languages.","url_abs":"https://arxiv.org/abs/2303.04134v2","url_pdf":"https://arxiv.org/pdf/2303.04134v2.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":"a-hybrid-architecture-for-out-of-domain","repo_url":"https://github.com/Makbari1997/VAE-KPCA-HDBSCAN","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"dimensionality-reduction","task_name":"Dimensionality Reduction"},{"task_slug":"intent-detection","task_name":"Intent Detection"},{"task_slug":"intent-discovery","task_name":"Intent Discovery"},{"task_slug":"natural-language-understanding","task_name":"Natural Language Understanding"},{"task_slug":"ood-detection","task_name":"Out of Distribution (OOD) Detection"},{"task_slug":"task-oriented-dialogue-systems","task_name":"Task-Oriented Dialogue Systems"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/intent-discovery-on-atis","task":"Intent Discovery","dataset":"ATIS","model":"k-PCA + HDBSCAN","rank_in_archive_order":1,"of":1,"metrics":{"ARI":"74.94"},"uses_additional_data":false},{"leaderboard":"/sota/intent-discovery-on-persian-atis","task":"Intent Discovery","dataset":"Persian-ATIS","model":"k-PCA + HDBSCAN","rank_in_archive_order":1,"of":1,"metrics":{"ARI":"11.97"},"uses_additional_data":false},{"leaderboard":"/sota/intent-discovery-on-snips","task":"Intent Discovery","dataset":"SNIPS","model":"k-PCA + HDBSCAN","rank_in_archive_order":1,"of":1,"metrics":{"ARI":"59.23"},"uses_additional_data":false},{"leaderboard":"/sota/out-of-distribution-ood-detection-on-atis","task":"Out of Distribution (OOD) Detection","dataset":"ATIS","model":"BERT + VAE","rank_in_archive_order":1,"of":1,"metrics":{"F1 - macro":"86.79"},"uses_additional_data":false},{"leaderboard":"/sota/out-of-distribution-ood-detection-on-persian","task":"Out of Distribution (OOD) Detection","dataset":"Persian-ATIS","model":"BERT + VAE","rank_in_archive_order":1,"of":1,"metrics":{"F1 Macro":"79.03"},"uses_additional_data":false},{"leaderboard":"/sota/out-of-distribution-ood-detection-on-snips","task":"Out of Distribution (OOD) Detection","dataset":"SNIPS","model":"BERT + VAE","rank_in_archive_order":1,"of":1,"metrics":{"F1 Macro":"92.32"},"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}