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.

Highlights

  • One of the core components of state-of-the-art and industrially deployed dialog systems is a dialog state tracker

  • Starting from these observations, we propose to formalize the task of state tracking as a particular instance of machine reading problem

  • As far as our knowledge goes, it is the first attempt to explicitly frame the task of dialog state tracking as a machine reading problem

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Summary

Introduction

One of the core components of state-of-the-art and industrially deployed dialog systems is a dialog state tracker. Its purpose is to provide a compact representation of a dialog produced from past user inputs and system outputs which is called the dialog state. C 2017 Association for Computational Linguistics the associated problem of statistical dialog state tracking with both the generative and discriminative approaches. At the end of this section, the limitations of the current models in terms of necessary annotations and reasoning capabilities are addressed.

Main Definitions
Generative Dialog State Tracking
Discriminative Dialog State Tracking
Current Limitations
Machine Reading Formulation of Dialog State Tracking
Machine Reading
End-to-End Memory Networks
Dialog Reading Model for State Tracking
Dataset and Data Preprocessing
Conclusion and Further Work
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