How Good Is NLP? A Sober Look at NLP Tasks through the Lens of Social Impact

Zhijing Jin, Geeticka Chauhan, Brian Tse, Mrinmaya Sachan, Rada Mihalcea


Anthology ID:
2021.findings-acl.273
Volume:
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
Month:
August
Year:
2021
Address:
Online
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3099–3113
Language:
URL:
https://aclanthology.org/2021.findings-acl.273
DOI:
10.18653/v1/2021.findings-acl.273
Bibkey:
Cite (ACL):
Zhijing Jin, Geeticka Chauhan, Brian Tse, Mrinmaya Sachan, and Rada Mihalcea. 2021. How Good Is NLP? A Sober Look at NLP Tasks through the Lens of Social Impact. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 3099–3113, Online. Association for Computational Linguistics.
Cite (Informal):
How Good Is NLP? A Sober Look at NLP Tasks through the Lens of Social Impact (Jin et al., Findings 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.findings-acl.273.pdf
Video:
 https://aclanthology.org/2021.findings-acl.273.mp4
Code
 zhijing-jin/NLP4SocialGood_Papers +  additional community code