@inproceedings{0e69b2681cd14108b42202afd0343e43,
title = "An end-to-end neural dialog state tracking for task-oriented dialogs",
abstract = "Dialog state tracking in spoken dialog system is the task that tracks the flow of a dialog and grasps what a user wants from the utterance precisely. Since the dialog success is related to catching the want of the user, dialog state tracking is a necessary component for spoken dialog systems. This paper proposes a neural dialog state tracker with the attention mechanism for focusing on valuable words and the hierarchical softmax for efficient training of the tracker. In addition, the proposed tracker combines a natural language understanding module and a dialog state module in an end-to-end style. As a result, the error propagation within a dialog system is minimized. To prove the effectiveness of the proposed model, we do experiments on dialog state tracking in the human-human task-oriented dialogs. Our experimental results show that the proposed method outperforms both the neural tracker without the attention mechanism and that without the hierarchical softmax.",
author = "Kim, \{A. Yeong\} and Kim, \{Tae Hyeong\} and Song, \{Hyun Je\} and Park, \{Seong Bae\}",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2018 ; Conference date: 08-07-2018 Through 13-07-2018",
year = "2018",
month = oct,
day = "12",
doi = "10.1109/FUZZ-IEEE.2018.8491471",
language = "English",
series = "IEEE International Conference on Fuzzy Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2018 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2018 - Proceedings",
}