SAD: A Stress Annotated Dataset for Recognizing Everyday Stressors in SMS-like Conversational Systems-Reference-Cited by-同舟云学术

SAD: A Stress Annotated Dataset for Recognizing Everyday Stressors in SMS-like Conversational Systems

Published:2021-05-08 Issue: Volume: Page:
ISSN:
Container-title:Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems
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Author:

Mauriello Matthew Louis¹,Lincoln Thierry²,Hon Grace²,Simon Dorien²,Jurafsky Dan³,Paredes Pablo⁴

Affiliation:

1. Department of Computer and Information Sciences University of Delaware, United States

2. Pervasive Wellbeing Technology Lab Stanford University, United States

3. Linguistics Stanford University, United States

4. School of Medicine Stanford University, United States

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3411763.3451799

Reference31 articles.

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3. Tom B. Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell Sandhini Agarwal Ariel Herbert-Voss Gretchen Krueger Tom Henighan Rewon Child Aditya Ramesh Daniel M. Ziegler Jeffrey Wu Clemens Winter Christopher Hesse Mark Chen Eric Sigler Mateusz Litwin Scott Gray Benjamin Chess Jack Clark Christopher Berner Sam McCandlish Alec Radford Ilya Sutskever and Dario Amodei. 2020. Language Models are Few-Shot Learners. arxiv:2005.14165 [cs.CL] Tom B. Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell Sandhini Agarwal Ariel Herbert-Voss Gretchen Krueger Tom Henighan Rewon Child Aditya Ramesh Daniel M. Ziegler Jeffrey Wu Clemens Winter Christopher Hesse Mark Chen Eric Sigler Mateusz Litwin Scott Gray Benjamin Chess Jack Clark Christopher Berner Sam McCandlish Alec Radford Ilya Sutskever and Dario Amodei. 2020. Language Models are Few-Shot Learners. arxiv:2005.14165 [cs.CL]

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