A Multimodal Model for Predicting Conversational Feedbacks

We propose in this paper a statistical model in the perspective of predicting listener’s feedbacks in a conversation. The first contribution of the paper is a study of the prediction of all feedbacks, including those in overlap with the speaker with a good accuracy. Existing model are good at predic...

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Bibliographic Details
Published inText, Speech, and Dialogue Vol. 12848; pp. 537 - 549
Main Authors Boudin, Auriane, Bertrand, Roxane, Rauzy, Stéphane, Ochs, Magalie, Blache, Philippe
Format Book Chapter
LanguageEnglish
Published Switzerland Springer International Publishing AG 2021
Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
Corpus study
Feedback
Linguistic interaction
Statistical model
Online AccessGet full text
ISBN303083526X
9783030835262
ISSN0302-9743
1611-3349
DOI10.1007/978-3-030-83527-9_46

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Summary:We propose in this paper a statistical model in the perspective of predicting listener’s feedbacks in a conversation. The first contribution of the paper is a study of the prediction of all feedbacks, including those in overlap with the speaker with a good accuracy. Existing model are good at predicting feedbacks during a pause, but reach a very low success level for all feedbacks. We give in this paper a first step towards this complex problem. The second contribution is a model predicting precisely the type of the feedback (generic vs. specific) as well as other specific features (valence expectation) useful in particular for generating feedbacks in dialogue systems. This work relies on an original corpus.
ISBN:303083526X
9783030835262
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-030-83527-9_46