Out-of-Domain Intent Detection Considering Multi-Turn Dialogue Contexts
International Conference on Language Resources and Evaluation(2023)
摘要
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.
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关键词
dialogue,contexts,out-of-domain,multi-turn
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