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
- Editors:
- Chengqing Zong, Fei Xia, Wenjie Li, Roberto Navigli
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2021.findings-acl.273.pdf
- Code
- zhijing-jin/NLP4SocialGood_Papers + additional community code