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A-Yeong Kim1, Hyun-Je Song2, Seong-Bae Park3

  • 1School of Computer Science and Engineering, Kyungpook National University, 80 Daehakro, Buk-gu 41566, Daegu, Republic of Korea.

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This study introduces a two-step neural dialog state tracker that improves spoken dialog systems by first identifying informative utterances and then tracking dialog states. The proposed model enhances accuracy and training speed for better user requirement identification.

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Machine Learning

Background:

  • Accurate dialog state tracking is crucial for spoken dialog system success.
  • Identifying user requirements from utterances is key to effective dialog flow.

Purpose of the Study:

  • To propose a novel two-step neural dialog state tracker.
  • To enhance the accuracy and efficiency of dialog state tracking in spoken dialog systems.

Main Methods:

  • Implemented a two-step approach: an informativeness classifier (CNN) and a neural tracker.
  • Utilized attention mechanism and hierarchical softmax for improved performance and faster training.
  • Conducted experiments on the DSTC4 dataset for human-human task-oriented dialogs.

Main Results:

  • The proposed two-step model significantly outperformed baseline neural trackers.
  • Effectiveness was demonstrated by comparing against models lacking the informativeness classifier, attention, or hierarchical softmax.
  • The model showed superior performance in dialog state tracking tasks.

Conclusions:

  • The proposed two-step neural dialog state tracker is effective for spoken dialog systems.
  • The integration of an informativeness classifier and specific neural network components enhances tracking accuracy and efficiency.
  • This approach offers a promising direction for advancing dialog state tracking research.