Dialog state tracking, a machine reading approach using Memory Network

Julien Perez, Fei Liu

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Abstract
In an end-to-end dialog system, the aim of dialog state tracking is to accurately estimate a compact representation of the current dialog status from a sequence of noisy observations produced by the speech recognition and the natural language understanding modules. This paper introduces a novel method of dialog state tracking based on the general paradigm of machine reading and proposes to solve it using an End-to-End Memory Network, MemN2N, a memory-enhanced neural network architecture. We evaluate the proposed approach on the second Dialog State Tracking Challenge (DSTC-2) dataset. The corpus has been converted for the occasion in order to frame the hidden state variable inference as a question-answering task based on a sequence of utterances extracted from a dialog. We show that the proposed tracker gives encouraging results. Then, we propose to extend the DSTC-2 dataset with specific reasoning capabilities requirement like counting, list maintenance, yes-no question answering and indefinite knowledge management. Finally, we present encouraging results using our proposed MemN2N based tracking model.
Anthology ID:
E17-1029
Volume:
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
Month:
April
Year:
2017
Address:
Valencia, Spain
Editors:
Mirella Lapata, Phil Blunsom, Alexander Koller
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
305–314
Language:
URL:
https://aclanthology.org/E17-1029
DOI:
Bibkey:
Cite (ACL):
Julien Perez and Fei Liu. 2017. Dialog state tracking, a machine reading approach using Memory Network. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 305–314, Valencia, Spain. Association for Computational Linguistics.
Cite (Informal):
Dialog state tracking, a machine reading approach using Memory Network (Perez & Liu, EACL 2017)
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https://preview.aclanthology.org/teach-a-man-to-fish/E17-1029.pdf
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