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<!DOCTYPE html>
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content="EmpathicStories++: A Multimodal Dataset for Empathy towards Personal Experiences">
<meta name="keywords" content="Health-LLM, Wearable Sensors, Personal Health Coach">
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<h1 class="title is-1 publication-title">EmpathicStories++: A Multimodal Dataset for Empathy towards Personal Experiences</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://jocelynshen.com/">Jocelyn Shen</a><sup>*</sup>,</span>
</span>
<span class="author-block">
<a href="https://ybkim95.github.io/">Yubin Kim</a><sup>*</sup>,
</span>
<span class="author-block">
<a href="">Mohit Hulse</a><sup></sup>,
</span>
<span class="author-block">
<a href="https://www.media.mit.edu/people/wazeer/overview/">Wazeer Zulfikar</a><sup></sup>,
</span>
<span class="author-block">
<a href="https://www.media.mit.edu/people/sharifah/overview/">Sharifa Alghowinem</a><sup></sup>,
</span>
<br>
<span class="author-block">
<a href="https://www.media.mit.edu/people/cynthiab/overview/">Cynthia Breazeal</a><sup></sup>,
</span>
<span class="author-block">
<a href="https://www.media.mit.edu/people/haewon/overview/">Hae Won Park</a><sup></sup>,
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup></sup>MIT Media Lab</span>
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<span>Paper</span>
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<a href="https://arxiv.org/pdf/2405.15708"
class="external-link button is-normal is-rounded is-dark">
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<span>arXiv</span>
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<h2 class="title is-3">Abstract</h2>
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<p>
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 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.
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<h2 class="title">Citation</h2>
<pre><code>@misc{shen2024empathicstories,
title={EmpathicStories++: A Multimodal Dataset for Empathy towards Personal Experiences},
author={Jocelyn Shen and Yubin Kim and Mohit Hulse and Wazeer Zulfikar and Sharifa Alghowinem and Cynthia Breazeal and Hae Won Park},
year={2024},
eprint={2405.15708},
archivePrefix={arXiv},
primaryClass={cs.CL}
}</code></pre>
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<h2 class="title">Requesting Access</h2>
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To have access to the dataset, please fill out this form: <a href="https://forms.gle/JfoLfiyeto7Zt9V86">Dataset Access Request Form</a>.
<br><br>Once you fill this form, you will be sent a Data Usage Agreement (DUA) from MIT.<br>
<b>You will need to sign and send the agreement</b>, upon which we will provide a link with the dataset download.
<br><br>
If you have any further questions, please email <a href="mailto:[email protected]">[email protected]</a> or <a href="mailto:[email protected]">[email protected]</a>.<br>
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This website is licensed under a <a rel="license"
href="http://creativecommons.org/licenses/by-sa/4.0/">Creative
Commons Attribution-ShareAlike 4.0 International License</a>. Credit to Keunhong Park for the website template.
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