EmoElicitor: An Open Domain Response Generation Model with User Emotional Reaction Awareness

Li, Shifeng; Feng, Shi; Wang, Daling; Song, Kaisong; Zhang, Yifei; Wang, Weichao

doi:10.24963/ijcai.2020/503

Cited by 16 publications

(16 citation statements)

References 9 publications

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“…Another explanation could be a gender bias in the emotion detector. Vokaturi's training databases 3 are the Berlin Database of Emotional Speech or Emo-DB 4 [2], which contains five female and five male speakers, and SAVEE 5 , which has voice samples of four males. This raises the question whether the training databases for emotion detection have to be gender-balanced.…”

Section: Discussion and Future Workmentioning

confidence: 99%

“…Research on how to react to emotion is mainly done by psychologists, but it typically investigates the communication between humans. Li et al [5] worked on emotional reaction by focusing on the feelings of the user and presented the 'EmoElicitor' model to elicit the particular emotions of users. Clos et al [3] tried to predict the emotional reaction of readers of social network posts.…”

Section: Related Workmentioning

confidence: 99%

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How Should Voice Assistants Deal With Users' Emotions?

Ma¹,

Drewes²,

Butz³

2022

Preprint

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There is a growing body of research in HCI on detecting the users' emotions. Once it is possible to detect users' emotions reliably, the next question is how an emotion-aware interface should react to the detected emotion. In a first step, we tried to find out how humans deal with negative emotions of an avatar. The hope behind this approach was to identify human strategies, which we can then mimic in an emotion-aware voice assistant. We present a user study in which participants were confronted with an angry, sad, or frightened avatar. Their task was to make the avatar happy by talking to it. We recorded the voice signal and analyzed it. The results show that users predominantly reacted with neutral emotion. However, we also found gender differences, which opens a range of questions.CCS Concepts: • Human-centered computing → Natural language interfaces.

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Section: Discussion and Future Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

How Should Voice Assistants Deal With Users' Emotions?

Ma¹,

Drewes²,

Butz³

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Early work target at emotional chatting and rely on emotional signals (Li et al, 2017;Zhou et al, 2018a;Wei et al, 2019;Zhou and Wang, 2018;Song et al, 2019). Later, some researchers shift focus towards eliciting user's specific emotion (Lubis et al, 2018;Li et al, 2020b). Recent work begin to incorporate extra information for deeper emotion understanding and empathetic responding (Lin et al, 2020;Li et al, 2020a;Roller et al, 2021).…”

Section: Emotion-aware Response Generationmentioning

confidence: 99%

MISC: A MIxed Strategy-Aware Model Integrating COMET for Emotional Support Conversation

Tu¹,

Li²,

Cui³

et al. 2022

Preprint

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Applying existing methods to emotional support conversation-which provides valuable assistance to people who are in need-has two major limitations: (a) they generally employ a conversation-level emotion label, which is too coarse-grained to capture user's instant mental state; (b) most of them focus on expressing empathy in the response(s) rather than gradually reducing user's distress. To address the problems, we propose a novel model MISC, which firstly infers the user's fine-grained emotional status, and then responds skillfully using a mixture of strategy. Experimental results on the benchmark dataset demonstrate the effectiveness of our method and reveal the benefits of fine-grained emotion understanding as well as mixed-up strategy modeling. Our code and data could be found in https://github. com/morecry/MISC.

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“…Desirable or future emotion of the user or sentiment-aware agent has also been explored. Zandie and Mahoor [39] encouraged the agent to learn an appropriate emotion for its response, whereas Li et al [12] conditioned their chatbot utterance on the desirable user emotion that the agent is trying to elicit. Within a reinforcement learning framework, Shin et al [29] rewarded response candidates likely to induce positive user emotion.…”

Section: Text-based Approaches To Computational Empathy In General Comentioning

confidence: 99%

Towards Detecting Need for Empathetic Response in Motivational Interviewing

Helaoui

Kumar

et al. 2020

Companion Publication of the 2020 International Conference on Multimodal Interaction

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Empathetic response from the therapist is key to the success of clinical psychotherapy, especially motivational interviewing. Previous work on computational modelling of empathy in motivational interviewing has focused on offline, session-level assessment of therapist empathy, where empathy captures all efforts that the therapist makes to understand the client's perspective and convey that understanding to the client. In this position paper, we propose a novel task of turn-level detection of client need for empathy. Concretely, we propose to leverage pre-trained language models and empathy-related general conversation corpora in a unique labellerdetector framework, where the labeller automatically annotates a motivational interviewing conversation corpus with empathy labels to train the detector that determines the need for therapist empathy. We also lay out our strategies of extending the detector with additional-input and multi-task setups to improve its detection and explainability. CCS CONCEPTS• Computing methodologies → Discourse, dialogue and pragmatics; Transfer learning; • Applied computing → Psychology.

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EmoElicitor: An Open Domain Response Generation Model with User Emotional Reaction Awareness

Cited by 16 publications

References 9 publications

How Should Voice Assistants Deal With Users' Emotions?

How Should Voice Assistants Deal With Users' Emotions?

MISC: A MIxed Strategy-Aware Model Integrating COMET for Emotional Support Conversation

Towards Detecting Need for Empathetic Response in Motivational Interviewing

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