@inproceedings{jagfeld-vu-2017-encoding,
title = "Encoding Word Confusion Networks with Recurrent Neural Networks for Dialog State Tracking",
author = "Jagfeld, Glorianna and
Vu, Ngoc Thang",
editor = "Ruiz, Nicholas and
Bangalore, Srinivas",
booktitle = "Proceedings of the Workshop on Speech-Centric Natural Language Processing",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-4602",
doi = "10.18653/v1/W17-4602",
pages = "10--17",
abstract = "This paper presents our novel method to encode word confusion networks, which can represent a rich hypothesis space of automatic speech recognition systems, via recurrent neural networks. We demonstrate the utility of our approach for the task of dialog state tracking in spoken dialog systems that relies on automatic speech recognition output. Encoding confusion networks outperforms encoding the best hypothesis of the automatic speech recognition in a neural system for dialog state tracking on the well-known second Dialog State Tracking Challenge dataset.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="jagfeld-vu-2017-encoding">
<titleInfo>
<title>Encoding Word Confusion Networks with Recurrent Neural Networks for Dialog State Tracking</title>
</titleInfo>
<name type="personal">
<namePart type="given">Glorianna</namePart>
<namePart type="family">Jagfeld</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ngoc</namePart>
<namePart type="given">Thang</namePart>
<namePart type="family">Vu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2017-09</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Workshop on Speech-Centric Natural Language Processing</title>
</titleInfo>
<name type="personal">
<namePart type="given">Nicholas</namePart>
<namePart type="family">Ruiz</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Srinivas</namePart>
<namePart type="family">Bangalore</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Copenhagen, Denmark</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>This paper presents our novel method to encode word confusion networks, which can represent a rich hypothesis space of automatic speech recognition systems, via recurrent neural networks. We demonstrate the utility of our approach for the task of dialog state tracking in spoken dialog systems that relies on automatic speech recognition output. Encoding confusion networks outperforms encoding the best hypothesis of the automatic speech recognition in a neural system for dialog state tracking on the well-known second Dialog State Tracking Challenge dataset.</abstract>
<identifier type="citekey">jagfeld-vu-2017-encoding</identifier>
<identifier type="doi">10.18653/v1/W17-4602</identifier>
<location>
<url>https://aclanthology.org/W17-4602</url>
</location>
<part>
<date>2017-09</date>
<extent unit="page">
<start>10</start>
<end>17</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Encoding Word Confusion Networks with Recurrent Neural Networks for Dialog State Tracking
%A Jagfeld, Glorianna
%A Vu, Ngoc Thang
%Y Ruiz, Nicholas
%Y Bangalore, Srinivas
%S Proceedings of the Workshop on Speech-Centric Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F jagfeld-vu-2017-encoding
%X This paper presents our novel method to encode word confusion networks, which can represent a rich hypothesis space of automatic speech recognition systems, via recurrent neural networks. We demonstrate the utility of our approach for the task of dialog state tracking in spoken dialog systems that relies on automatic speech recognition output. Encoding confusion networks outperforms encoding the best hypothesis of the automatic speech recognition in a neural system for dialog state tracking on the well-known second Dialog State Tracking Challenge dataset.
%R 10.18653/v1/W17-4602
%U https://aclanthology.org/W17-4602
%U https://doi.org/10.18653/v1/W17-4602
%P 10-17
Markdown (Informal)
[Encoding Word Confusion Networks with Recurrent Neural Networks for Dialog State Tracking](https://aclanthology.org/W17-4602) (Jagfeld & Vu, 2017)
ACL