International technical interventions in agriculture and rural development: Some basic trends, issues, and questions

In recent years, there has been an increasing interest in the application of Artificial Intelligence -and especially Machine Learningto the field of Sustainable Development (SD). However, until now, NLP has not been systematically applied in this context. In this paper, we show the high potential of NLP to enhance project sustainability. 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. Here, 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 extreme multi-class multi-label Automatic User-Perceived Value classification task. We release Stories2Insights (S2I), an expertannotated dataset of interviews carried out in Uganda, we provide a detailed corpus analysis, and we implement a number of strong neural baselines to address the task. Experimental results show that the problem is challenging, and leaves considerable room for future research at the intersection of NLP and SD.

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International technical interventions in agriculture and rural development: Some basic trends, issues, and questions

Cited by 3 publications

References 11 publications

Information technology in the Costa Rican dairy sector: A key instrument in extension and on-farm research

Information technology in the Costa Rican dairy sector: A key instrument in extension and on-farm research

Responsible innovation through conscious contestation at the interface of agricultural science, policy, and civil society

Natural Language Processing for Achieving Sustainable Development: the Case of Neural Labelling to Enhance Community Profiling

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