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%0 Conference Proceedings
%T How Good Is NLP? A Sober Look at NLP Tasks through the Lens of Social Impact
%A Jin, Zhijing
%A Chauhan, Geeticka
%A Tse, Brian
%A Sachan, Mrinmaya
%A Mihalcea, Rada
%Y Zong, Chengqing
%Y Xia, Fei
%Y Li, Wenjie
%Y Navigli, Roberto
%S Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F jin-etal-2021-good
%R 10.18653/v1/2021.findings-acl.273
%U https://aclanthology.org/2021.findings-acl.273
%U https://doi.org/10.18653/v1/2021.findings-acl.273
%P 3099-3113

Markdown (Informal)

[How Good Is NLP? A Sober Look at NLP Tasks through the Lens of Social Impact](https://aclanthology.org/2021.findings-acl.273) (Jin et al., Findings 2021)

• How Good Is NLP? A Sober Look at NLP Tasks through the Lens of Social Impact (Jin et al., Findings 2021)

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.