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 this https URL . We released our data at
this https URL and code at
this https URL .

该论文介绍了 NLP4SGPAPERS 数据集及其三项关联任务，即识别解决社会问题的论文、映射至联合国可持续发展目标、识别解决任务和使用方法，并使用最先进的 NLP 模型分别处理这些任务，帮助研究人员全面了解 NLP4SG 领域。

超越良好意愿：报告面向社会公益的自然语言处理研究领域

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

Progress on the UN Sustainable Development Goals (SDGs) is hampered by a
persistent lack of data regarding key social, environmental, and economic
indicators, particularly in developing countries. For example, data on poverty
--- the first of seventeen SDGs --- is both spatially sparse and infrequently
collected in Sub-Saharan Africa due to the high cost of surveys. Here we
propose a novel method for estimating socioeconomic indicators using
open-source, geolocated textual information from Wikipedia articles. We
demonstrate that modern NLP techniques can be used to predict community-level
asset wealth and education outcomes using nearby geolocated Wikipedia articles.
When paired with nightlights satellite imagery, our method outperforms all
previously published benchmarks for this prediction task, indicating the
potential of Wikipedia to inform both research in the social sciences and
future policy decisions.

该研究介绍了利用开放源代码的地理定位维基百科文章中的文本信息，运用现代 NLP 技术，预测社区层面的资产财富和教育成果的新方法，提高普查数据收集的成本效益，优于先前的数据预测基准，能为社会科学研究和政策决策提供参考。