An integrated model for predicting backchannel feedbacks

Blache, Philippe; Abderrahmane, Massina; Rauzy, Stéphane; Bertrand, Roxane

doi:10.1145/3383652.3423948

Cited by 11 publications

(5 citation statements)

References 16 publications

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“…1 As its names indicate, specific BC is a reaction to the content of the speaker's utterance. It might indicate the listener's agreement/disagreement, surprise, fear, etc (Blache et al, 2020). As for generic BC, it is performed to show that the listener is paying attention to the speaker and keeping up with the conversation without conveying a narrative content to the speaker (see examples in Table 2).…”

Section: Introductionmentioning

confidence: 99%

Backchannel Behavior in Child-Caregiver Video Calls

Bodur¹,

Nikolaus²,

Fourtassi³

2022

Preprint

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An important step in children’s socio-cognitive development is learning how to engage in coordinated conversations. This requires not only becoming competent speakers but also active listeners. This paper studies children’s use of backchannel signaling (e.g., ”yeah!” or a head nod) when in the listener’s role during conversations with their caregivers via Zoom. While previous work had found backchannel to be still immature in middle childhood, our use of both more natural/spontaneous conversational settings and more adequate controls allowed us to reveal that school-age children are strikingly close to adult-level mastery in many measures of backchanneling. The broader impact of this paper is to highlight the crucial role of social context in evaluating children’s conversational abilities.

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Section: Introductionmentioning

confidence: 99%

Backchannel Behavior in Child-Caregiver Video Calls

Bodur¹,

Nikolaus²,

Fourtassi³

2022

Preprint

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show abstract

“…Predictive features5,20,34,22,12,38]), verbal (lexico-syntactic features[21,10,18]) and visual (gestures, expressions, attitudes,[1,17,22,25]). Besides widely used features (e.g.…”

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confidence: 99%

A Multimodal Model for Predicting Conversational Feedbacks

Boudin

Bertrand

Rauzy

et al. 2021

Text, Speech, and Dialogue

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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.

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“…In our feedback model, besides lowlevel classical cues (such as breaks, turn length, POS, etc. ), we also integrate higher level semantic or discourse-level cues [3]. The dialogue systems mainly have two input streams: the audio signal and its transcription.…”

Section: Conversational Feedbacksmentioning

confidence: 99%

Common Ground, Frames and Slots for Comprehension in Dialogue Systems

Blache¹,

Houlès²

2021

Natural Language Computing

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This paper presents a dialogue system for training doctors to break bad news. The originality of this work lies in its knowledge representation. All information known before the dialogue (the universe of discourse, the context, the scenario of the dialogue) as well as the knowledge transferred from the doctor to the patient during the conversation is represented in a shared knowledge structure called common ground, that constitute the core of the system. The Natural Language Understanding and the Natural Language Generation modules of the system take advantage on this structure and we present in this paper different original techniques making it possible to implement them efficiently.

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An integrated model for predicting backchannel feedbacks

Cited by 11 publications

References 16 publications

Backchannel Behavior in Child-Caregiver Video Calls

Backchannel Behavior in Child-Caregiver Video Calls

A Multimodal Model for Predicting Conversational Feedbacks

Common Ground, Frames and Slots for Comprehension in Dialogue Systems

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