Computational Approach to Understanding Empathy

Empathy Framework Paper Code and Data Slides EMNLP Talk

Empathic Rewriting

Best Paper Award (TheWebConf/WWW 2021)

Empathic Rewriting

Paper Code Slides TheWebConf Talk
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What is this work about?

Online peer-to-peer support platforms such as TalkLife enable conversations between millions of people who seek and provide mental health support. If successful, web-based mental health conversations could improve access to treatment and reduce the global disease burden. Psychologists have repeatedly demonstrated that empathy, the ability to understand and feel the emotions and experiences of others, is a key component leading to positive outcomes in supportive conversations. Computational methods that can empower non-expert peer-supporters with empathy-based feedback and training has the potential to help them express higher levels of empathy and in turn, improve the effectiveness of online support platforms.

Our team at the Paul G. Allen School of Computer Science & Engineering, in collaboration with clinical psychologists from the UW and Stanford Medical Schools, is working on creating computational methods for helping peer supporters express empathy more effectively in conversations. We are developing AI tools that can identify and improve empathy in conversations and give intelligent and actionable real-time feedback to users. Here's a summary of our contributions:


Citation

EMNLP

Ashish Sharma, Adam S Miner, David C Atkins, & Tim Althoff (2020).
A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support. EMNLP

@inproceedings{sharma2020empathy,
title={A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support},
author={Sharma, Ashish and Miner, Adam S and Atkins, David C and Althoff, Tim},
year={2020},
booktitle={EMNLP}
}

TheWebConf

Ashish Sharma, Inna W Lin, Adam S Miner, David C Atkins, & Tim Althoff (2021).
Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning Approach. WWW/TheWebConf

@inproceedings{sharma2021facilitating,
title={Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning Approach},
author={Sharma, Ashish and Lin, Inna W and Miner, Adam S and Atkins, David C and Althoff, Tim},
year={2021},
booktitle={TheWebConf/WWW}
}

Team



Acknowledgements


We would like to thank TalkLife and Jamie Druitt for their support and for providing us access to a TalkLife dataset. We also thank the members of UW Behavioral Data Science group, UW NLP group, Zac E. Imel, and the anonymous reviewers for their feedback on this work. A.S. and T.A. were supported in part by NSF grant IIS-1901386, Bill & Melinda Gates Foundation (INV-004841), an Adobe Data Science Research Award, the Allen Institute for Artificial Intelligence, and a Microsoft AI for Accessibility grant. A.S.M. was supported by grants from the National Institutes of Health, National Center for Advancing Translational Science, Clinical and Translational Science Award (KL2TR001083 and UL1TR001085) and the Stanford Human-Centered AI Institute. D.C.A. was supported in part by a NIAAA K award (K02AA023814).

Conflict of Interest Disclosure: Dr. Atkins is a cofounder with equity stake in a technology company, Lyssn.io, focused on tools to support training, supervision, and quality assurance of psychotherapy and counseling.