EmoEM: Emotional Expression in a Multi-turn Dialogue Model

Zhang, Ao; Wu, Shaojuan; Zhang, Xiaowang; Chen, Shizhan; Shu, Yuchun; Feng, Zhiyong

doi:10.1109/ictai50040.2020.00083

Cited by 3 publications

(2 citation statements)

References 20 publications

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“…The model update training is achieved by parameterizing the policy through the policy gradient algorithm, to maximize the future cumulative reward expectation by optimizing the model parameters. Therefore, the objective function is to maximize the expected value of future rewards, defined as (11) where denotes the reward value obtained by acting in the state ; then the gradient is updated using the likelihood ratio technique (12) Finally, the parameter is updated using the obtained gradient values…”

Section: Model Optimizationmentioning

confidence: 99%

“…In the literature [9][10], the cognitive emotion model of the robot is integrated into the smart home environment, and the cognitive reassessment strategy guided by positive emotion is obtained by optimizing and analyzing the cognitive emotion model of the service robot in the smart home environment using simulated annealing algorithm, and the probability of transferring emotional states is updated based on the cognitive reassessment strategy. Literature [11][12] proposed the multiemotion dialogue system MECS, which tends to generate coherent emotional responses in dialogue and selects the most similar emotion as the robot response emotion. The literature [13][14] proposes emotional chat machines that can produce appropriate responses not only in terms of content relevance and syntax but also in terms of emotional coherence.…”

Section: Introductionmentioning

confidence: 99%

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Innovation of college pop music teaching in traditional music culture based on robot cognitive-emotional interaction model

Zhang¹

2023

3C TIC

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Emotional computing and artificial psychology is a new research direction that has received increasing attention in the field of harmonious human-computer interaction and artificial intelligence and is also a new intersection of mathematics, information science, intelligence science, neuroscience, physiology, psychological science and other multidisciplinary intersection. The current problems and drawbacks in the teaching of popular music in colleges and universities, and the search for methods and measures to reform and innovate popular music education in colleges and universities are the difficulties of current music teaching work. In this paper, we try to apply a robot cognitive-emotional interaction model to college pop music teaching, and establish an emotional interaction model based on reinforcement learning with the help of cognitive-emotional computing of human-computer interaction, to be able to integrate emotional interaction in pop music teaching and to make an accurate emotional analysis of students' singing effect. Different from traditional music teaching methods, the robot-based cognitive-emotional interaction model established in this paper can establish an innovative teaching model for college pop music teaching and optimize the teaching effect.

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Section: Model Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Innovation of college pop music teaching in traditional music culture based on robot cognitive-emotional interaction model

Zhang¹

2023

3C TIC

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RAG and Few-Shot Prompting in Emotional Text Generation

Vologina,

Matveeva,

Makhnytkina

et al. 2024

Lecture Notes in Computer Science

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Emotion inference in conversations based on commonsense enhancement and graph structures

Zhang,

Xu,

Xie

et al. 2024

PLoS ONE

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In the task of emotion inference, a common issue is the lack of common sense knowledge, particularly in the context of dialogue, where traditional research has failed to effectively extract structural features, resulting in lower accuracy in emotion inference. To address this, this paper proposes a dialogue emotion inference model based on Common Sense Enhancement and Graph Model (CEICG). This model integrates external common sense with graph model techniques by dynamically constructing nodes and defining diverse edge relations to simulate the evolution of dialogue, thereby effectively capturing the structural and semantic features of the conversation. The model employs two methods to incorporate external common sense into the graph model, overcoming the limitations of previous models in understanding complex dialogue structures and the absence of external knowledge. This strategy of integrating external common sense significantly enhances the model’s emotion inference capabilities, improving the understanding of emotions in dialogue. Experimental results demonstrate that the CEICG model outperforms six existing baseline models in emotion inference tasks across three datasets.

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EmoEM: Emotional Expression in a Multi-turn Dialogue Model

Cited by 3 publications

References 20 publications

Innovation of college pop music teaching in traditional music culture based on robot cognitive-emotional interaction model

Innovation of college pop music teaching in traditional music culture based on robot cognitive-emotional interaction model

RAG and Few-Shot Prompting in Emotional Text Generation

Emotion inference in conversations based on commonsense enhancement and graph structures

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