Year: 2018
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Authors: Xia, Congying, Zhang, Chenwei, Yan, Xiaohui, Chang, Yi, Yu, Philip S.
Institution: University of Illinois Chicago
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: III: Medium: Collaborative Research: An Extensible Heterogeneous Network Embedding Framework with Application Specific Adaptation, G3473395741, G3703353469, 跨网络链接预测研究, G722514612, MRI: Acquisition of SABER: Shared Analytics and Big-data Enterprise Resource, III: Small: Fusion of Heterogeneous Networks for Synergistic Knowledge Discovery
OpenAlex ID: W2889660205