EmpathicStories++: A Multimodal Dataset for Empathy towards Personal Experiences
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866929357591674880 |
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| author | Shen, Jocelyn Kim, Yubin Hulse, Mohit Zulfikar, Wazeer Alghowinem, Sharifa Breazeal, Cynthia Park, Hae Won |
| author_facet | Shen, Jocelyn Kim, Yubin Hulse, Mohit Zulfikar, Wazeer Alghowinem, Sharifa Breazeal, Cynthia Park, Hae Won |
| contents | Modeling empathy is a complex endeavor that is rooted in interpersonal and experiential dimensions of human interaction, and remains an open problem within AI. Existing empathy datasets fall short in capturing the richness of empathy responses, often being confined to in-lab or acted scenarios, lacking longitudinal data, and missing self-reported labels. We introduce a new multimodal dataset for empathy during personal experience sharing: the EmpathicStories++ dataset (https://mitmedialab.github.io/empathic-stories-multimodal/) containing 53 hours of video, audio, and text data of 41 participants sharing vulnerable experiences and reading empathically resonant stories with an AI agent. EmpathicStories++ is the first longitudinal dataset on empathy, collected over a month-long deployment of social robots in participants' homes, as participants engage in natural, empathic storytelling interactions with AI agents. We then introduce a novel task of predicting individuals' empathy toward others' stories based on their personal experiences, evaluated in two contexts: participants' own personal shared story context and their reflections on stories they read. We benchmark this task using state-of-the-art models to pave the way for future improvements in contextualized and longitudinal empathy modeling. Our work provides a valuable resource for further research in developing empathetic AI systems and understanding the intricacies of human empathy within genuine, real-world settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_15708 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | EmpathicStories++: A Multimodal Dataset for Empathy towards Personal Experiences Shen, Jocelyn Kim, Yubin Hulse, Mohit Zulfikar, Wazeer Alghowinem, Sharifa Breazeal, Cynthia Park, Hae Won Computation and Language Modeling empathy is a complex endeavor that is rooted in interpersonal and experiential dimensions of human interaction, and remains an open problem within AI. Existing empathy datasets fall short in capturing the richness of empathy responses, often being confined to in-lab or acted scenarios, lacking longitudinal data, and missing self-reported labels. We introduce a new multimodal dataset for empathy during personal experience sharing: the EmpathicStories++ dataset (https://mitmedialab.github.io/empathic-stories-multimodal/) containing 53 hours of video, audio, and text data of 41 participants sharing vulnerable experiences and reading empathically resonant stories with an AI agent. EmpathicStories++ is the first longitudinal dataset on empathy, collected over a month-long deployment of social robots in participants' homes, as participants engage in natural, empathic storytelling interactions with AI agents. We then introduce a novel task of predicting individuals' empathy toward others' stories based on their personal experiences, evaluated in two contexts: participants' own personal shared story context and their reflections on stories they read. We benchmark this task using state-of-the-art models to pave the way for future improvements in contextualized and longitudinal empathy modeling. Our work provides a valuable resource for further research in developing empathetic AI systems and understanding the intricacies of human empathy within genuine, real-world settings. |
| title | EmpathicStories++: A Multimodal Dataset for Empathy towards Personal Experiences |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2405.15708 |