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Multi-level Feature Combination in Dialogue State Tracking

  • Yang ZhengEmail author
  • Ruifang Liu
  • Sheng Gao
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1074)

Abstract

Dialogue State Tracking is one of the most important component in task-oriented dialog system, which can update the dialogue state and accurately estimate the compact state representation. This paper introduces a multi-level feature combination to capture correlation on the basis of the entire dialog and slots. Based on the Memory-Network [1], which only consider the relevance of current user utterance with historical dialogue context under turn level, we both consider the correlation at the sentence-level and word-level, and give the slot-value pair as input replacing of candidate state pool to estimate the rare slot-value pair. We use Bi-LSTM and self-attention to capture the correlation between internal words from the utterance, the particular slot-value pair and previous unsettled actions, and finally give a confidence degree score to judge whether the particular slot-value pair is the right answer for the current utterance. Our model achieves a good result on the DSTC-2 dataset about 0.74, which is 3% higher than Memory-Network’s 0.71.

Keywords

Task-oriented dialog system Dialogue state tracking Memory-Network Multi-level feature combination 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Beijing University of Posts and TelecommunicationsBeijingChina

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