@article{sun-etal-2016-recurrent,
title = "Recurrent Polynomial Network for Dialogue State Tracking",
author = "Sun, Kai and
Xie, Qizhe and
Yu, Kai",
editor = "Poesio, Massimo and
Eugenio, Barbara Di and
Schlangen, David and
Williams, Jason D. and
Raux, Antoine and
Henderson, Matthew and
Ginzburg, Jonathan",
journal = "Dialogue {\&} Discourse",
volume = "7",
month = apr,
year = "2016",
address = "Bielefeld, Germany",
publisher = "University of Bielefeld",
url = "https://aclanthology.org/2016.dnd-7.2/",
doi = "10.5087/dad.2016.303",
pages = "65--88",
abstract = "Dialogue state tracking (DST) is a process to estimate the distribution of the dialogue states as a dialogue progresses. Recent studies on constrained Markov Bayesian polynomial (CMBP) framework take the first step towards bridging the gap between rule-based and statistical approaches for DST. In this paper, the gap is further bridged by a novel framework {--} recurrent polynomial network (RPN). RPN{'}s unique structure enables the framework to have all the advantages of CMBP including efficiency, portability and interpretability. Additionally, RPN achieves more properties of statistical approaches than CMBP. RPN was evaluated on the data corpora of the second and the third Dialog State Tracking Challenge (DSTC-2/3). Experiments showed that RPN can significantly outperform both traditional rule-based approaches and statistical approaches with similar feature set. Compared with the state-of-the-art statistical DST approaches with a lot richer features, RPN is also competitive."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="sun-etal-2016-recurrent">
<titleInfo>
<title>Recurrent Polynomial Network for Dialogue State Tracking</title>
</titleInfo>
<name type="personal">
<namePart type="given">Kai</namePart>
<namePart type="family">Sun</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Qizhe</namePart>
<namePart type="family">Xie</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kai</namePart>
<namePart type="family">Yu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2016-04</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<genre authority="bibutilsgt">journal article</genre>
<relatedItem type="host">
<titleInfo>
<title>Dialogue & Discourse</title>
</titleInfo>
<originInfo>
<issuance>continuing</issuance>
<publisher>University of Bielefeld</publisher>
<place>
<placeTerm type="text">Bielefeld, Germany</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">periodical</genre>
<genre authority="bibutilsgt">academic journal</genre>
</relatedItem>
<abstract>Dialogue state tracking (DST) is a process to estimate the distribution of the dialogue states as a dialogue progresses. Recent studies on constrained Markov Bayesian polynomial (CMBP) framework take the first step towards bridging the gap between rule-based and statistical approaches for DST. In this paper, the gap is further bridged by a novel framework – recurrent polynomial network (RPN). RPN’s unique structure enables the framework to have all the advantages of CMBP including efficiency, portability and interpretability. Additionally, RPN achieves more properties of statistical approaches than CMBP. RPN was evaluated on the data corpora of the second and the third Dialog State Tracking Challenge (DSTC-2/3). Experiments showed that RPN can significantly outperform both traditional rule-based approaches and statistical approaches with similar feature set. Compared with the state-of-the-art statistical DST approaches with a lot richer features, RPN is also competitive.</abstract>
<identifier type="citekey">sun-etal-2016-recurrent</identifier>
<identifier type="doi">10.5087/dad.2016.303</identifier>
<location>
<url>https://aclanthology.org/2016.dnd-7.2/</url>
</location>
<part>
<date>2016-04</date>
<detail type="volume"><number>7</number></detail>
<extent unit="page">
<start>65</start>
<end>88</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Journal Article
%T Recurrent Polynomial Network for Dialogue State Tracking
%A Sun, Kai
%A Xie, Qizhe
%A Yu, Kai
%J Dialogue & Discourse
%D 2016
%8 April
%V 7
%I University of Bielefeld
%C Bielefeld, Germany
%F sun-etal-2016-recurrent
%X Dialogue state tracking (DST) is a process to estimate the distribution of the dialogue states as a dialogue progresses. Recent studies on constrained Markov Bayesian polynomial (CMBP) framework take the first step towards bridging the gap between rule-based and statistical approaches for DST. In this paper, the gap is further bridged by a novel framework – recurrent polynomial network (RPN). RPN’s unique structure enables the framework to have all the advantages of CMBP including efficiency, portability and interpretability. Additionally, RPN achieves more properties of statistical approaches than CMBP. RPN was evaluated on the data corpora of the second and the third Dialog State Tracking Challenge (DSTC-2/3). Experiments showed that RPN can significantly outperform both traditional rule-based approaches and statistical approaches with similar feature set. Compared with the state-of-the-art statistical DST approaches with a lot richer features, RPN is also competitive.
%R 10.5087/dad.2016.303
%U https://aclanthology.org/2016.dnd-7.2/
%U https://doi.org/10.5087/dad.2016.303
%P 65-88
Markdown (Informal)
[Recurrent Polynomial Network for Dialogue State Tracking](https://aclanthology.org/2016.dnd-7.2/) (Sun et al., DND 2016)
ACL