论文标题
实现可持续发展的自然语言处理：神经标签以增强社区概况的案例
Natural language processing for achieving sustainable development: the case of neural labelling to enhance community profiling
论文作者
论文摘要
近年来，人们对可持续发展（SD）领域的人工智能（尤其是机器学习）的应用越来越兴趣。但是，到目前为止，在这种情况下还没有应用NLP。在这篇研究论文中，我们显示了NLP应用程序在增强项目可持续性方面具有很高的潜力。特别是，我们专注于发展中国家社区分析的情况，与发达国家相比，存在一个显着的数据差距。在这种情况下，NLP可以帮助解决结构性数据的成本和时间障碍，以禁止其广泛使用和相关的收益。我们提出了自动UPV分类的新任务，这是一个极端的多级多标签分类问题。我们发布Stories2Insights是一项专家注册的数据集，提供了详细的语料库分析，并实施了许多强大的神经基线来解决任务。实验结果表明，这个问题具有挑战性，并在NLP和SD的交集中留出了很多空间进行未来的研究。
In recent years, there has been an increasing interest in the application of Artificial Intelligence - and especially Machine Learning - to the field of Sustainable Development (SD). However, until now, NLP has not been applied in this context. In this research paper, we show the high potential of NLP applications to enhance the sustainability of projects. In particular, we focus on the case of community profiling in developing countries, where, in contrast to the developed world, a notable data gap exists. In this context, NLP could help to address the cost and time barrier of structuring qualitative data that prohibits its widespread use and associated benefits. We propose the new task of Automatic UPV classification, which is an extreme multi-class multi-label classification problem. We release Stories2Insights, an expert-annotated dataset, provide a detailed corpus analysis, and implement a number of strong neural baselines to address the task. Experimental results show that the problem is challenging, and leave plenty of room for future research at the intersection of NLP and SD.
