Causal Relationships Between Emotions and Dialog Acts

Cao, Shuyi; Qu, Lizhen; Tian, Leimin

doi:10.1109/acii52823.2021.9597428

Cited by 2 publications

(3 citation statements)

References 25 publications

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“…Indeed, in real world cases, dependencies among time series are usually nonlinear and ignoring such interactions could lead to inconsistent estimation. However, using causal knowledge to improve machine learning algorithms remains an open area [ 98 , 100 ], and causal analysis in affective computing is at best in its infancy apart from a handful of exceptions (e.g., [ 100 , 101 , 102 , 103 , 104 , 105 ]).…”

Section: Discussionmentioning

confidence: 99%

Inferring Causal Factors of Core Affect Dynamics on Social Participation through the Lens of the Observer

D’Amelio

Patania

Buršić

et al. 2023

Sensors

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A core endeavour in current affective computing and social signal processing research is the construction of datasets embedding suitable ground truths to foster machine learning methods. This practice brings up hitherto overlooked intricacies. In this paper, we consider causal factors potentially arising when human raters evaluate the affect fluctuations of subjects involved in dyadic interactions and subsequently categorise them in terms of social participation traits. To gauge such factors, we propose an emulator as a statistical approximation of the human rater, and we first discuss the motivations and the rationale behind the approach.The emulator is laid down in the next section as a phenomenological model where the core affect stochastic dynamics as perceived by the rater are captured through an Ornstein–Uhlenbeck process; its parameters are then exploited to infer potential causal effects in the attribution of social traits. Following that, by resorting to a publicly available dataset, the adequacy of the model is evaluated in terms of both human raters’ emulation and machine learning predictive capabilities. We then present the results, which are followed by a general discussion concerning findings and their implications, together with advantages and potential applications of the approach.

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Section: Discussionmentioning

confidence: 99%

Inferring Causal Factors of Core Affect Dynamics on Social Participation through the Lens of the Observer

D’Amelio

Patania

Buršić

et al. 2023

Sensors

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show abstract

“…For instance, Happiness of the speaker's utterance has a great chance of causing the DA of appreciation in the following response. The DA of signal-non-understanding and backchannel-question-form usually raise surprise [4]. Apart from emotions expressed through speech, affective bursts, especially laughter, are paralinguistic events that occur frequently in spontaneous dialogues [29,43].…”

Section: Related Workmentioning

confidence: 99%

“…The emotion prediction model can be built by drawing on prior knowledge of emotion-emotion mapping [21] and the DA-emotion causal relationship [4]. We conducted a preliminary analysis demonstrating that in human-robot dialogue, the users indeed mimic the robot's emotion, but the correlations show different patterns in the arousal and valence dimensions (described in Sec.…”

Section: Proposed Architecture For An Anticipatory Sds 31 the Speech ...mentioning

confidence: 99%

I Know Your Feelings Before You Do: Predicting Future Affective Reactions in Human-Computer Dialogue

Inoue

Tian

et al. 2023

Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems

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Current Spoken Dialogue Systems (SDSs) often serve as passive listeners that respond only after receiving user speech. To achieve human-like dialogue, we propose a novel future prediction architecture that allows an SDS to anticipate future affective reactions based on its current behaviors before the user speaks. In this work, we investigate two scenarios: speech and laughter. In speech, we propose to predict the user's future emotion based on its temporal relationship with the system's current emotion and its causal relationship with the system's current Dialogue Act (DA). In laughter, we propose to predict the occurrence and type of the user's laughter using the system's laughter behaviors in the current turn. Preliminary analysis of human-robot dialogue demonstrated synchronicity in the emotions and laughter displayed by the human and robot, as well as DA-emotion causality in their dialogue. This verifies that our architecture can contribute to the development of an anticipatory SDS. CCS CONCEPTS• Human-centered computing → Human computer interaction (HCI).

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Causal Relationships Between Emotions and Dialog Acts

Cited by 2 publications

References 25 publications

Inferring Causal Factors of Core Affect Dynamics on Social Participation through the Lens of the Observer

Inferring Causal Factors of Core Affect Dynamics on Social Participation through the Lens of the Observer

I Know Your Feelings Before You Do: Predicting Future Affective Reactions in Human-Computer Dialogue

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