Beyond Good Intentions: Reporting the Research Landscape of NLP for Social Good (2023)

Abstract: With the recent advances in natural language processing (NLP), a vast number of applications have emerged across various use cases. Among the plethora of NLP applications, many academic researchers are motivated to do work that has a positive social impact, in line with the recent initiatives of NLP for Social Good (NLP4SG). However, it is not always obvious to researchers how their research efforts are tackling today’s big social problems. Thus, in this paper, we introduce NLP4SGPapers, a scientific dataset with three associated tasks that can help identify NLP4SG papers and characterize the NLP4SG landscape by: (1) identifying the papers that address a social problem, (2) mapping them to the corresponding UN Sustainable Development Goals (SDGs), and (3) identifying the task they are solving and the methods they are using. Using state-of-the-art NLP models, we address each of these tasks and use them on the entire ACL Anthology, resulting in a visualization workspace that gives researchers a comprehensive overview of the field of NLP4SG. Our website is available at https://nlp4sg.vercel.app . We released our data at https://huggingface.co/datasets/feradauto/NLP4SGPapers and code at https://github.com/feradauto/nlp4sg

## introduction with the rapid advancement of natural language processing (nlp) technology @xcite @xcite , there has been a growing interest in using nlp for applications with social impact in recent years @xcite @xcite . this effort has been condensed into the initiative of "nlp for social good" (nlp4sg) @xcite , which began with an overall theoretical framework ## nlp4sgpapers dataset we build a nlp4sgpapers dataset to address the following three tasks as illustrated in figure 1: • task 1: binary classification of nlp4sg papers (for q1). next, we introduce the data collection process, and then analyze the data statistics for each task. ## nlp4sg paperanalyzer our paperanalyzer system works as shown in figure 1 , with three sequential steps: we first train a binary classifier (addressing task 1), followed by a un sdg goal classifier (addressing task 2), and finally processing the paper with a pretrained task and method extractor (addressing task 3).