Dialog state tracking, a machine reading approach using Memory Network

Published:

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, Valencia, Spain, pp. 305-314.

@InProceedings{Perez+:2017,
  author    = {Perez, Julien  and  Liu, Fei},
  title     = {Dialog state tracking, a machine reading approach using Memory Network},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics},
  year      = {2017},
  address   = {Valencia, Spain},
  pages     = {305--314}
}

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.