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1. Modeling Empathetic Alignment in Conversation

   https://doi.org/10.18653/v1/2024.naacl-long.172  

   Yang, Jiamin; Jurgens, David (June 2024, Association for Computational Linguistics)
2. A Comparative Multidimensional Analysis of Empathetic Systems

   Lee, Andrew; Kummerfeld, Jonathan; An, Larry; Mihalcea, Rada (March 2024, Association for Computational Linguistics)
3. Empathetic Robot with Transformer-Based Di-alogue Agent

   Xie, Baijun; Park, Chung (July 2021, International Conference on Ubiquitous Robots (UR))

   Natural Human-Robot interaction (HRI) attracts considerable interest in letting robots understand the users’ emotional state. This paper demonstrates a method to introduce the affection model to the robotic system’s conversational agent to provide natural and empathetic HRI. We use a large-scale pre-trained language model and fine-tune it on a dialogue dataset with empathetic characteristics. Based on existing studies’ progress, we extend the current method and enable the agent to perform advanced sentiment analysis using the affection model. This dialogue agent will allow the robot to provide natural response along with emotion classification and the estimations of arousal and valence level. We evaluate our model using different metrics, comparing it with the recent studies and showing its emotion detection capacity. 

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4. An Empathetic Social Robot with Modular Anxiety Interventions for Autistic Adolescents

   Xie, B; Park, C H (August 2024, 2024 33rd IEEE International Conference on Robot and Human Interactive Communication (ROMAN))
5. Are You Feeling Happy? the Effect of Emotions on People’s Interaction Experience Toward Empathetic Chatbots

   https://doi.org/10.1115/DETC2022-91059  

   Liao, Ting; Yan, Bei (July 2022, ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference)

   Abstract People may experience emotions before interacting with automated agents to seek information and support. However, existing literature has not well examined how human emotional states affect their interaction experience with agents or how automated agents should react to emotions. This study proposes to test how participants perceive an empathetic agent (chatbot) vs. a non-empathetic one under various emotional states (i.e., positive, neutral, negative) when the chatbot mediates the initial screening process for student advising. Participants are prompted to recall a previous emotional experience and have text-based conversations with the chatbot. The study confirms the importance of presenting empathetic cues in the design of automated agents to support human-agent collaboration. Participants who recall a positive experience are more sensitive to the chatbot’s empathetic behavior. The empathetic behavior of the chatbot improves participants’ satisfaction and makes those who recall a neutral experience feel more positive during the interaction. The results reveal that participants’ emotional states are likely to influence their tendency to self-disclose, interaction experience, and perception of the chatbot’s empathetic behavior. The study also highlights the increasing need for emotional acknowledgment of people who experience positive emotions so that design efforts need to be designated according to people’s dynamic emotional states. 

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