Supervised Clustering of Questions into Intents for Dialog System Applications

Iryna Haponchyk, Antonio Uva, Seunghak Yu, Olga Uryupina, Alessandro Moschitti


Abstract
Modern automated dialog systems require complex dialog managers able to deal with user intent triggered by high-level semantic questions. In this paper, we propose a model for automatically clustering questions into user intents to help the design tasks. Since questions are short texts, uncovering their semantics to group them together can be very challenging. We approach the problem by using powerful semantic classifiers from question duplicate/matching research along with a novel idea of supervised clustering methods based on structured output. We test our approach on two intent clustering corpora, showing an impressive improvement over previous methods for two languages/domains.
Anthology ID:
D18-1254
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
2310–2321
Language:
URL:
https://aclanthology.org/D18-1254
DOI:
10.18653/v1/D18-1254
Bibkey:
Cite (ACL):
Iryna Haponchyk, Antonio Uva, Seunghak Yu, Olga Uryupina, and Alessandro Moschitti. 2018. Supervised Clustering of Questions into Intents for Dialog System Applications. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2310–2321, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Supervised Clustering of Questions into Intents for Dialog System Applications (Haponchyk et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1254.pdf
Video:
 https://vimeo.com/305938531