Understand
The latency in the current neural based dialogue state tracking models prohibits them from being used efficiently for deployment in production systems, albeit their highly accurate performance.
- This paper proposes a new scalable and accurate neural dialogue state tracking model, based on the recently proposed Global-Local Self-Attention encoder (GLAD) model by Zhong et al.
- which uses global modules to share parameters between estimators for different types (called slots) of dialogue states, and uses local modules to learn slot-specific features.
- By using only one recurrent networks with global conditioning, compared to (1 + \# slots) recurrent networks with global and local conditioning used in the GLAD model, our proposed model reduces the latency in training and inference times by $35\%$ on average, while preserving performance of belief state tracking, by $97.38\%$ on turn request and $88.51\%$ on joint goal and accuracy.
Built on
Long short-term memory
S. Hochreiter and J. Schmidhuber · 1997
Earlier work this paper cites.
The dialog state tracking challenge
J. D. Williams, A. Raux, D. Ramachandran, and A. W. Black · 2013
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Glove: Global vectors for word representation
J. Pennington, R. Socher, and C. Manning · 2014
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A joint many-task model: Growing a neural network for multiple nlp tasks
K. Hashimoto, C. Xiong, Y. Tsuruoka, and R. Socher · 2017
Earlier work this paper cites.
Similar
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Then
Multiwoz - a large-scale multi-domain wizard-of-oz dataset for task-oriented dialogue modelling
P. Budzianowski, T.-H. Wen, B.-H. Tseng, I. Casanueva, S. Ultes, O. Ramadan, and M. Gavsi’c · 2018
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An end-to-end approach for handling unknown slot values in dialogue state tracking
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