Dialogue Act Recognition Using Visual Information

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F21%3A43963769" target="_blank" >RIV/49777513:23520/21:43963769 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scopus.com/record/display.uri?origin=resultslist&eid=2-s2.0-85115300974" target="_blank" >https://www.scopus.com/record/display.uri?origin=resultslist&eid=2-s2.0-85115300974</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-86331-9_51" target="_blank" >10.1007/978-3-030-86331-9_51</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Dialogue Act Recognition Using Visual Information

Popis výsledku v původním jazyce

Automatic dialogue management including dialogue act (DA) recognition is usually focused on dialogues in the audio signal. However, some dialogues are also available in a written form and their automatic analysis is also very important. The main goal of this paper thus consists in the dialogue act recognition from printed documents. For visual DA recognition, we propose a novel deep model that combines two recurrent neural networks. The approach is evaluated on a newly created dataset containing printed dialogues from the English VERBMOBIL corpus. We have shown that visual information does not have any positive impact on DA recognition using good quality images where the OCR result is excellent. We have also demonstrated that visual information can significantly improve the DA recognition score on low-quality images with erroneous OCR. To the best of our knowledge, this is the first attempt focused on DA recognition from visual data.

Název v anglickém jazyce

Dialogue Act Recognition Using Visual Information

Popis výsledku anglicky

Automatic dialogue management including dialogue act (DA) recognition is usually focused on dialogues in the audio signal. However, some dialogues are also available in a written form and their automatic analysis is also very important. The main goal of this paper thus consists in the dialogue act recognition from printed documents. For visual DA recognition, we propose a novel deep model that combines two recurrent neural networks. The approach is evaluated on a newly created dataset containing printed dialogues from the English VERBMOBIL corpus. We have shown that visual information does not have any positive impact on DA recognition using good quality images where the OCR result is excellent. We have also demonstrated that visual information can significantly improve the DA recognition score on low-quality images with erroneous OCR. To the best of our knowledge, this is the first attempt focused on DA recognition from visual data.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/EF17_048%2F0007267" target="_blank" >EF17_048/0007267: VaV inteligentních komponent pokročilých technologií pro plzeňskou metropolitní oblast</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Document Analysis and Recognition - ICDAR 2021

ISBN

978-3-030-86330-2

ISSN

0302-9743

e-ISSN

1611-3349

Počet stran výsledku

15

Strana od-do

793-807

Název nakladatele

Springer

Místo vydání

Heidelberg

Místo konání akce

Lausanne, Švýcarsko

Datum konání akce

5. 9. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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