May 8, 2023, 12:44 a.m. | Hao Lang, Yinhe Zheng, Binyuan Hui, Fei Huang, Yongbin Li

cs.CL updates on arXiv.org arxiv.org

Out-of-Domain (OOD) intent detection is vital for practical dialogue systems,
and it usually requires considering multi-turn dialogue contexts. However, most
previous OOD intent detection approaches are limited to single dialogue turns.
In this paper, we introduce a context-aware OOD intent detection (Caro)
framework to model multi-turn contexts in OOD intent detection tasks.
Specifically, we follow the information bottleneck principle to extract robust
representations from multi-turn dialogue contexts. Two different views are
constructed for each input sample and the superfluous information …

arxiv context detection dialogue framework intent detection paper practical systems

AI Research Scientist

@ Vara | Berlin, Germany and Remote

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

AI Engineering Manager

@ M47 Labs | Barcelona, Catalunya [Cataluña], Spain