How Good Is NLP? A Sober Look at NLP Tasks through the Lens of Social Impact
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
LLINBO: Trustworthy LLM-in-the-Loop Bayesian Optimization
Streaming 4D Visual Geometry Transformer
Simple is what you need for efficient and accurate medical image segmentation
Time Series Forecasting as Reasoning: A Slow-Thinking Approach with Reinforced LLMs
Sorrel: A simple and flexible framework for multi-agent reinforcement learning
On Denoising Walking Videos for Gait Recognition
토론
이 논문과 연결된 포럼 글이 아직 없습니다.