@inproceedings{06c8332fdff2482293b849e008742399,
title = "Neural dialog state tracker for large ontologies by attention mechanism",
abstract = "This paper presents a dialog state tracker submitted to Dialog State Tracking Challenge 5 (DSTC 5) with details. To tackle the challenging cross-language human-human dialog state tracking task with limited training data, we propose a tracker that focuses on words with meaningful context based on attention mechanism and bi-directional long short term memory (LSTM). The vocabulary including a plenty of proper nouns is vectorized with a sufficient amount of related texts crawled from web to learn a good embedding for words not existent in training dialogs. Despite its simplicity, our proposed tracker succeeded to achieve high accuracy without sophisticated pre- and post-processing.",
keywords = "Attention mechanism, Dialog state tracking, DSTC5, Recurrent Neural Network, Word embedding",
author = "Youngsoo Jang and Jiyeon Ham and Lee, {Byung Jun} and Youngjae Chang and Kim, {Kee Eung}",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE Workshop on Spoken Language Technology, SLT 2016 ; Conference date: 13-12-2016 Through 16-12-2016",
year = "2017",
month = feb,
day = "7",
doi = "10.1109/SLT.2016.7846314",
language = "English",
series = "2016 IEEE Workshop on Spoken Language Technology, SLT 2016 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "531--537",
booktitle = "2016 IEEE Workshop on Spoken Language Technology, SLT 2016 - Proceedings",
}