Cross-Language Neural Dialog State Tracker for Large Ontologies Using Hierarchical Attention

Youngsoo Jang, Jiyeon Ham, Byung Jun Lee, Kee Eung Kim

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

Dialog state tracking, which refers to identifying the user intent from utterances, is one of the most important tasks in dialog management. In this paper, we present our dialog state tracker developed for the fifth dialog state tracking challenge, which focused on cross-language adaptation using a very scarce machine-translated training data when compared to the size of the ontology. Our dialog state tracker is based on the bi-directional long short-term memory network with a hierarchical attention mechanism in order to spot important words in user utterances. The user intent is predicted by finding the closest keyword in the ontology to the attention-weighted word vector. With the suggested methodology, our tracker can overcome various difficulties due to the scarce training data that existing machine learning-based trackers had, such as predicting user intents they have not seen before. We show that our tracker outperforms other trackers submitted to the challenge with respect to most of the performance measures.

Original languageEnglish
Article number8401898
Pages (from-to)2072-2082
Number of pages11
JournalIEEE/ACM Transactions on Audio Speech and Language Processing
Volume26
Issue number11
DOIs
Publication statusPublished - 2018 Nov
Externally publishedYes

Keywords

  • attention mechanism
  • cross language
  • Dialog state tracking
  • hierarchical attention mechanism
  • long short term memory

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Acoustics and Ultrasonics
  • Computational Mathematics
  • Electrical and Electronic Engineering

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