Zero-shot User Intent Detection via Capsule Neural Networks

Congying Xia, Chenwei Zhang, Xiaohui Yan, Yi Chang, Philip Yu


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: IntentCapsNet 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.
Anthology ID:
D18-1348
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Editors:
Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
3090–3099
Language:
URL:
https://aclanthology.org/D18-1348
DOI:
10.18653/v1/D18-1348
Bibkey:
Cite (ACL):
Congying Xia, Chenwei Zhang, Xiaohui Yan, Yi Chang, and Philip Yu. 2018. Zero-shot User Intent Detection via Capsule Neural Networks. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3090–3099, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Zero-shot User Intent Detection via Capsule Neural Networks (Xia et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1348.pdf
Video:
 https://aclanthology.org/D18-1348.mp4
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