@article{serban-etal-2018-survey,
title = "A Survey of Available Corpora For Building Data-Driven Dialogue Systems: The Journal Version",
author = "Serban, Iulian Vlad and
Lowe, Ryan and
Henderson, Peter and
Charlin, Laurent and
Pineau, Joelle",
editor = "Traum, David and
Demberg, Vera and
Stent, Amanda and
Taboada, Maite and
Stede, Manfred and
Poesio, Massimo",
journal = "Dialogue {\&} Discourse",
volume = "9",
month = may,
year = "2018",
address = "Bielefeld, Germany",
publisher = "University of Bielefeld",
url = "https://aclanthology.org/2018.dnd-9.7/",
doi = "10.5087/dad.2018.101",
pages = "1--49",
abstract = "During the past decade, several areas of speech and language understanding have witnessed substantial breakthroughs from the use of data-driven models. In the area of dialogue systems, the trend is less obvious, and most practical systems are still built through significant engineering and expert knowledge. Nevertheless, several recent results suggest that data-driven approaches are feasible and quite promising. To facilitate research in this area, we have carried out a wide survey of publicly available datasets suitable for data-driven learning of dialogue systems. We discuss important characteristics of these datasets, how they can be used to learn diverse dialogue strategies, and their other potential uses. We also examine methods for transfer learning between datasets and the use of external knowledge. Finally, we discuss appropriate choice of evaluation metrics for the learning objective."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="serban-etal-2018-survey">
<titleInfo>
<title>A Survey of Available Corpora For Building Data-Driven Dialogue Systems: The Journal Version</title>
</titleInfo>
<name type="personal">
<namePart type="given">Iulian</namePart>
<namePart type="given">Vlad</namePart>
<namePart type="family">Serban</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ryan</namePart>
<namePart type="family">Lowe</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Peter</namePart>
<namePart type="family">Henderson</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Laurent</namePart>
<namePart type="family">Charlin</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Joelle</namePart>
<namePart type="family">Pineau</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2018-05</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>During the past decade, several areas of speech and language understanding have witnessed substantial breakthroughs from the use of data-driven models. In the area of dialogue systems, the trend is less obvious, and most practical systems are still built through significant engineering and expert knowledge. Nevertheless, several recent results suggest that data-driven approaches are feasible and quite promising. To facilitate research in this area, we have carried out a wide survey of publicly available datasets suitable for data-driven learning of dialogue systems. We discuss important characteristics of these datasets, how they can be used to learn diverse dialogue strategies, and their other potential uses. We also examine methods for transfer learning between datasets and the use of external knowledge. Finally, we discuss appropriate choice of evaluation metrics for the learning objective.</abstract>
<identifier type="citekey">serban-etal-2018-survey</identifier>
<identifier type="doi">10.5087/dad.2018.101</identifier>
<location>
<url>https://aclanthology.org/2018.dnd-9.7/</url>
</location>
<part>
<date>2018-05</date>
<detail type="volume"><number>9</number></detail>
<extent unit="page">
<start>1</start>
<end>49</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Journal Article
%T A Survey of Available Corpora For Building Data-Driven Dialogue Systems: The Journal Version
%A Serban, Iulian Vlad
%A Lowe, Ryan
%A Henderson, Peter
%A Charlin, Laurent
%A Pineau, Joelle
%J Dialogue & Discourse
%D 2018
%8 May
%V 9
%I University of Bielefeld
%C Bielefeld, Germany
%F serban-etal-2018-survey
%X During the past decade, several areas of speech and language understanding have witnessed substantial breakthroughs from the use of data-driven models. In the area of dialogue systems, the trend is less obvious, and most practical systems are still built through significant engineering and expert knowledge. Nevertheless, several recent results suggest that data-driven approaches are feasible and quite promising. To facilitate research in this area, we have carried out a wide survey of publicly available datasets suitable for data-driven learning of dialogue systems. We discuss important characteristics of these datasets, how they can be used to learn diverse dialogue strategies, and their other potential uses. We also examine methods for transfer learning between datasets and the use of external knowledge. Finally, we discuss appropriate choice of evaluation metrics for the learning objective.
%R 10.5087/dad.2018.101
%U https://aclanthology.org/2018.dnd-9.7/
%U https://doi.org/10.5087/dad.2018.101
%P 1-49
Markdown (Informal)
[A Survey of Available Corpora For Building Data-Driven Dialogue Systems: The Journal Version](https://aclanthology.org/2018.dnd-9.7/) (Serban et al., DND 2018)
ACL