@inproceedings{conforti-etal-2020-natural,
title = "Natural Language Processing for Achieving Sustainable Development: the Case of Neural Labelling to Enhance Community Profiling",
author = "Conforti, Costanza and
Hirmer, Stephanie and
Morgan, Dai and
Basaldella, Marco and
Ben Or, Yau",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.677",
doi = "10.18653/v1/2020.emnlp-main.677",
pages = "8427--8444",
abstract = "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 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 UserPerceived Value classification task. We release Stories2Insights, an expert-annotated 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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<abstract>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 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 UserPerceived Value classification task. We release Stories2Insights, an expert-annotated 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.</abstract>
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%0 Conference Proceedings
%T Natural Language Processing for Achieving Sustainable Development: the Case of Neural Labelling to Enhance Community Profiling
%A Conforti, Costanza
%A Hirmer, Stephanie
%A Morgan, Dai
%A Basaldella, Marco
%A Ben Or, Yau
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F conforti-etal-2020-natural
%X 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 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 UserPerceived Value classification task. We release Stories2Insights, an expert-annotated 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.
%R 10.18653/v1/2020.emnlp-main.677
%U https://aclanthology.org/2020.emnlp-main.677
%U https://doi.org/10.18653/v1/2020.emnlp-main.677
%P 8427-8444
Markdown (Informal)
[Natural Language Processing for Achieving Sustainable Development: the Case of Neural Labelling to Enhance Community Profiling](https://aclanthology.org/2020.emnlp-main.677) (Conforti et al., EMNLP 2020)
ACL