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

本文提出了两种基于胶囊网络的架构，分别为 INTENT-CAPSNET 和 INTENTCAPSNET-ZSL，可用于用户意图检测问题。使用这些模型可以在没有标记的语句的情况下检测出新兴的用户意图。在两个真实世界数据集上的实验证明了该模型的有效性。