Recent years have seen many breakthroughs in natural language processing (NLP), transitioning it from a mostly theoretical field to one with vast real-world applications. Noting precursor applications in other machine learning and AI techniques with pervasive societal impact, we anticipate the rising importance of developing NLP technologies for social good. Inspired by theories in moral philosophy and global priority research, we aim to promote a future guideline for social good in the context of NLP. We lay the foundations via \textit{moral philosophy}'s definition of social good, and propose a framework to provide a categorization of NLP tasks on the basis of real-world impact, along with metrics to calculate the expected social impact of NLP technology. Based on these fundamental frameworks, we adopt the methodology of global priority research to identify priority causes for NLP research, and use thought experiments to illustrate research priority under hypothetical situations. Finally, we use our theoretical framework to quantify ACL 2020 papers on the basis of societal impact, and provide a checklist for ensuring the creation of NLP technologies that advance social good. Based on these tools, we envision new usage of NLP to effectively improve social good and call attention to important NLP problems with the potential for strong social impact.

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

Recent years have seen many breakthroughs in natural language processing (NLP), transitioning it from a mostly theoretical field to one with many real-world applications. Noting the rising number of applications of other machine learning and AI techniques with pervasive societal impact, we anticipate the rising importance of developing NLP technologies for social good. Inspired by theories in moral philosophy and global priorities research, we aim to promote a guideline for social good in the context of NLP. We lay the foundations via moral philosophy's definition of social good, propose a framework to evaluate NLP tasks' direct and indirect real-world impact, and adopt the methodology of global priorities research to identify priority causes for NLP research. Finally, we use our theoretical framework to provide some practical guidelines for future NLP research for social good. Our data and codes are available at http://github.com/zhijing-jin/nlp4sg_acl2021

Findings Papers - Theme

technical paper

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IJCNLP-AACL 2021

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

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

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

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