PCMID: Multi-Intent Detection through Supervised Prototypical Contrastive Learning

Yurun song, Junchen Zhao, Spencer B. Koehler, Amir Abdullah, Ian Harris

Submission Type: Regular Long Paper

Submission Track: Dialogue and Interactive Systems

Submission Track 2: NLP Applications

Keywords: Dialogue System, Multi-Intent Detection

Abstract: Intent detection is a major task in Natural Language Understanding (NLU) and is the component of dialogue systems for interpreting users’ intentions based on their utterances. Many works have explored detecting intents by assuming that each utterance represents only a single intent. Such systems have achieved very good results; however, intent detection is a far more challenging task in typical real-world scenarios, where each user utterance can be highly complex and express multiple intents. Therefore, in this paper, we propose PCMID, a novel Multi-Intent Detection framework enabled by Prototypical Contrastive Learning under a supervised setting. The PCMID model can learn multiple semantic representations of a given user utterance under the context of different intent labels in an optimized semantic space. Our experiments show that PCMID achieves the current state-of-the-art performance on both multiple public benchmark datasets and a private real-world dataset for the multi-intent detection task.

Submission Number: 9