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

• URL: http://arxiv.org/abs/2106.02359v1
• Date: Fri, 4 Jun 2021 09:17:15 GMT
• Title: How Good Is NLP? A Sober Look at NLP Tasks through the Lens of Social Impact
• Authors: Zhijing Jin, Geeticka Chauhan, Brian Tse, Mrinmaya Sachan, Rada Mihalcea
• Abstract summary: We propose a framework to evaluate NLP tasks' direct and indirect real-world impact. We 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.
• Score: 31.435252562175194
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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

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