Out-of-Domain Intent Detection Considering Multi-turn Dialogue Contexts

Hao Lang, Yinhe Zheng, Binyuan Hui, Fei Huang, Yongbin Li

Submission Type: Regular Long Paper

Submission Track: Machine Learning for NLP

Keywords: OOD Detection, Multi-turn Dialogue Contexts

TL;DR: OOD Intent Detection Considering Multi-turn Dialogue Contexts

Abstract:

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 not related to intent detection is removed using a multi-view information bottleneck loss.
Moreover, we also explore utilizing unlabeled data in Caro.
A two-stage training process is introduced to mine OOD samples from these unlabeled data,
and these OOD samples are used to train the resulting model with a bootstrapping approach.
Comprehensive experiments demonstrate that Caro establishes state-of-the-art performances on multi-turn OOD detection tasks by improving the F1-OOD score of over 29% compared to the previous best method.

Submission Number: 62