Dialogue act recognition approaches
Identifieur interne : 001C47 ( Hal/Curation ); précédent : 001C46; suivant : 001C48Dialogue act recognition approaches
Auteurs : Pavel Kral [France] ; Christophe Cerisara [France]Source :
- Computing and Informatics [ 1335-9150 ] ; 2010.
Abstract
This paper deals with automatic dialogue act (DA) recognition. Dialogue acts are sentence-level units that represent states of a dialogue, such as questions, statements, hesitations, etc. The knowledge of dialogue act realizations in a discourse or dialogue is part of the speech understanding and dialogue analysis process. It is of great importance for many applications: dialogue systems, speech recognition, automatic machine translation, etc. The main goal of this paper is to study the existing works about DA recognition and to discuss their respective advantages and drawbacks. A major concern in the DA recognition domain is that, although a few DA annotation schemes seem now to emerge as standards, most of the time, these DA tag-sets have to be adapted to the specificities of a given application, which prevents the deployment of standardized DA databases and evaluation procedures. The focus of this review is put on the various kinds of information that can be used to recognize DAs, such as prosody, lexical, etc., and on the types of models proposed so far to capture this information. Combining these information sources tends to appear nowadays as a prerequisite to recognize DAs.
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nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Institut national polytechnique de Lorraine</orgName><orgName type="institution" wicri:auto="newGroup">Université de Lorraine</orgName></affiliation></author></analytic><series><title level="j">Computing and Informatics</title><idno type="ISSN">1335-9150</idno><imprint><date type="datePub">2010</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">This paper deals with automatic dialogue act (DA) recognition. Dialogue acts are sentence-level units that represent states of a dialogue, such as questions, statements, hesitations, etc. The knowledge of dialogue act realizations in a discourse or dialogue is part of the speech understanding and dialogue analysis process. It is of great importance for many applications: dialogue systems, speech recognition, automatic machine translation, etc. The main goal of this paper is to study the existing works about DA recognition and to discuss their respective advantages and drawbacks. A major concern in the DA recognition domain is that, although a few DA annotation schemes seem now to emerge as standards, most of the time, these DA tag-sets have to be adapted to the specificities of a given application, which prevents the deployment of standardized DA databases and evaluation procedures. The focus of this review is put on the various kinds of information that can be used to recognize DAs, such as prosody, lexical, etc., and on the types of models proposed so far to capture this information. Combining these information sources tends to appear nowadays as a prerequisite to recognize DAs.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="en">Dialogue act recognition approaches</title><author role="aut"><persName><forename type="first">Pavel</forename><surname>Kral</surname></persName><email>kral@loria.fr</email><idno type="halauthor">66669</idno><affiliation ref="#struct-2359"></affiliation></author><author role="aut"><persName><forename type="first">Christophe</forename><surname>Cerisara</surname></persName><email>christophe.cerisara@loria.fr</email><idno type="idhal">christophe-cerisara</idno><idno type="halauthor">59131</idno><affiliation ref="#struct-2359"></affiliation></author><editor role="depositor"><persName><forename>Christophe</forename><surname>Cerisara</surname></persName><email>cerisara@loria.fr</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2009-11-12 10:44:57</date><date type="whenModified">2016-05-18 09:00:23</date><date type="whenReleased">2009-11-12 10:44:57</date><date type="whenProduced">2010</date></edition><respStmt><resp>contributor</resp><name key="103171"><persName><forename>Christophe</forename><surname>Cerisara</surname></persName><email>cerisara@loria.fr</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">inria-00431396</idno><idno type="halUri">https://hal.inria.fr/inria-00431396</idno><idno type="halBibtex">kral:inria-00431396</idno><idno type="halRefHtml">Computing and Informatics, Slovak University Press, Bratislava, 2010, 29 (2), pp.227--250</idno><idno type="halRef">Computing and Informatics, Slovak University Press, Bratislava, 2010, 29 (2), pp.227--250</idno></publicationStmt><seriesStmt><idno type="stamp" n="CNRS">CNRS - Centre national de la recherche scientifique</idno><idno type="stamp" n="INRIA">INRIA - Institut National de Recherche en Informatique et en Automatique</idno><idno type="stamp" n="INPL">Institut National Polytechnique de Lorraine</idno><idno type="stamp" n="LORIA2">Publications du LORIA</idno><idno type="stamp" n="INRIA-NANCY-GRAND-EST">INRIA Nancy - Grand Est</idno><idno type="stamp" n="LORIA">LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications</idno><idno type="stamp" n="LORIA-TALC" p="LORIA">Traitement automatique des langues et des connaissances</idno><idno type="stamp" n="INRIA2">INRIA 2</idno><idno type="stamp" n="INRIA-LORRAINE">INRIA Nancy - Grand Est</idno><idno type="stamp" n="LABO-LORIA-SET" p="LORIA">LABO-LORIA-SET</idno><idno type="stamp" n="UNIV-LORRAINE">Université de Lorraine</idno></seriesStmt><notesStmt><note type="audience" n="2">International</note><note type="popular" n="0">No</note><note type="peer" n="1">Yes</note></notesStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Dialogue act recognition approaches</title><author role="aut"><persName><forename type="first">Pavel</forename><surname>Kral</surname></persName><email>kral@loria.fr</email><idno type="halAuthorId">66669</idno><affiliation ref="#struct-2359"></affiliation></author><author role="aut"><persName><forename type="first">Christophe</forename><surname>Cerisara</surname></persName><email>christophe.cerisara@loria.fr</email><idno type="idHal">christophe-cerisara</idno><idno type="halAuthorId">59131</idno><affiliation ref="#struct-2359"></affiliation></author></analytic><monogr><idno type="halJournalId" status="VALID">23954</idno><idno type="issn">1335-9150</idno><title level="j">Computing and Informatics</title><imprint><publisher>Slovak University Press, Bratislava</publisher><biblScope unit="volume">29</biblScope><biblScope unit="issue">2</biblScope><biblScope unit="pp">227--250</biblScope><date type="datePub">2010</date></imprint></monogr></biblStruct></sourceDesc><profileDesc><langUsage><language ident="en">English</language></langUsage><textClass><classCode scheme="halTypology" n="ART">Journal articles</classCode></textClass><abstract xml:lang="en">This paper deals with automatic dialogue act (DA) recognition. Dialogue acts are sentence-level units that represent states of a dialogue, such as questions, statements, hesitations, etc. The knowledge of dialogue act realizations in a discourse or dialogue is part of the speech understanding and dialogue analysis process. It is of great importance for many applications: dialogue systems, speech recognition, automatic machine translation, etc. The main goal of this paper is to study the existing works about DA recognition and to discuss their respective advantages and drawbacks. A major concern in the DA recognition domain is that, although a few DA annotation schemes seem now to emerge as standards, most of the time, these DA tag-sets have to be adapted to the specificities of a given application, which prevents the deployment of standardized DA databases and evaluation procedures. The focus of this review is put on the various kinds of information that can be used to recognize DAs, such as prosody, lexical, etc., and on the types of models proposed so far to capture this information. Combining these information sources tends to appear nowadays as a prerequisite to recognize DAs.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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