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User intent detection plays a critical role in question-answering and dialogsystems. Most previous works treat intent detection as a classification problemwhere utterances are labeled with predefined intents. However, it islabor-intensive and time-consuming to label users' utterances as intents arediversely expressed and novel intents will continually be involved. Instead, westudy the zero-shot intent detection problem, which aims to detect emerginguser intents where no labeled utterances are currently available. We proposetwo capsule-based architectures: INTENT-CAPSNET that extracts semantic featuresfrom utterances and aggregates them to discriminate existing intents, andINTENTCAPSNET-ZSL which gives INTENTCAPSNET the zero-shot learning ability todiscriminate emerging intents via knowledge transfer from existing intents.Experiments on two real-world datasets show that our model not only can betterdiscriminate diversely expressed existing intents, but is also able todiscriminate emerging intents when no labeled utterances are available.

Zero-shot User Intent Detection via Capsule Neural Networks