  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 the moral philosophy definition of social good, propose a
framework to evaluate the direct and indirect real-world impact of NLP tasks,
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
code are available at http://github.com/zhijing-jin/nlp4sg_acl2021. In
addition, we curate a list of papers and resources on NLP for social good at
https://github.com/zhijing-jin/NLP4SocialGood_Papers.
