Simple, Fast, Accurate Intent Classification and Slot Labeling for Goal-Oriented Dialogue Systems

Arshit Gupta, John Hewitt, Katrin Kirchhoff


Abstract
With the advent of conversational assistants, like Amazon Alexa, Google Now, etc., dialogue systems are gaining a lot of traction, especially in industrial setting. These systems typically consist of Spoken Language understanding component which, in turn, consists of two tasks - Intent Classification (IC) and Slot Labeling (SL). Generally, these two tasks are modeled together jointly to achieve best performance. However, this joint modeling adds to model obfuscation. In this work, we first design framework for a modularization of joint IC-SL task to enhance architecture transparency. Then, we explore a number of self-attention, convolutional, and recurrent models, contributing a large-scale analysis of modeling paradigms for IC+SL across two datasets. Finally, using this framework, we propose a class of ‘label-recurrent’ models that otherwise non-recurrent, with a 10-dimensional representation of the label history, and show that our proposed systems are easy to interpret, highly accurate (achieving over 30% error reduction in SL over the state-of-the-art on the Snips dataset), as well as fast, at 2x the inference and 2/3 to 1/2 the training time of comparable recurrent models, thus giving an edge in critical real-world systems.
Anthology ID:
W19-5906
Volume:
Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue
Month:
September
Year:
2019
Address:
Stockholm, Sweden
Editors:
Satoshi Nakamura, Milica Gasic, Ingrid Zukerman, Gabriel Skantze, Mikio Nakano, Alexandros Papangelis, Stefan Ultes, Koichiro Yoshino
Venue:
SIGDIAL
SIG:
SIGDIAL
Publisher:
Association for Computational Linguistics
Note:
Pages:
46–55
Language:
URL:
https://aclanthology.org/W19-5906
DOI:
10.18653/v1/W19-5906
Bibkey:
Cite (ACL):
Arshit Gupta, John Hewitt, and Katrin Kirchhoff. 2019. Simple, Fast, Accurate Intent Classification and Slot Labeling for Goal-Oriented Dialogue Systems. In Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue, pages 46–55, Stockholm, Sweden. Association for Computational Linguistics.
Cite (Informal):
Simple, Fast, Accurate Intent Classification and Slot Labeling for Goal-Oriented Dialogue Systems (Gupta et al., SIGDIAL 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-5906.pdf
Data
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