Natural Language Processing for Achieving Sustainable Development: the Case of Neural Labelling to Enhance Community Profiling (2020)

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.

## introduction sustainable development (sd) is an interdisciplinary field which studies the integration and balancing of economic, environmental and social concerns to tackle the broad goal of achieving inclusive and sustainable growth @xcite @xcite . as a collective, trans-national effort toward sustainability, in 2015 the united nations approved the 2030 agenda (united nations, 2015), which identifies 17 sustainable development goals (sdgs) to be reached by 2030 @xcite . in recent years, there has been increasing recognition of the fundamental role played by data in achieving the objectives set out in the sdgs @xcite @xcite . in this paper, we focus on data-driven planning and delivery of projects foot_0 which address one or more of the sdgs in a developing country context. when dealing with developing countries, a deep understanding of project beneficiaries' needs and values (hereafter referred to as user-perceived values or upvs, @xcite ) is of particular importance. this is because beneficiaries with limited financial means are especially good at assessing needs and values @xcite . when a project fails to create value to a benefiting community, the community is less likely to care about its continued operation @xcite @xcite and as a consequence, the chances of the project's longterm success is jeopardised @xcite . therefore, comprehensive community profiling 2 plays a key role in understanding what is important for a community and act upon it, thus ensuring a project's sustainability @xcite . obtaining data with such characteristics requires knowledge extraction from qualitative interviews which come in the form of unstructured free text @xcite . this step is usually done manually by domain experts @xcite , which further raises the costs. thus, structured qualitative data is often unaffordable for project developers. as a consequence, project planning heavily relies upon sub-optimal aggregated statistical data, like household surveys @xcite or remotely-sensed satellite imagery @xcite , which unfortu-nately is of considerable lower resolution in developing countries. whilst these quantitative data sets are important and necessary, they are insufficient to ensure successful project design, lacking insights on upvs that are crucial to success. in this context, the application of nlp techniques can help to make qualitative data more accessible to project developers by dramatically reducing time and costs to structure data. however, despite having been successfully applied to many other domains -ranging from biomedicine @xcite , to law @xcite and finance @xcite -to our knowledge, nlp has not yet been applied to the field of sd in a systematic and academically rigorous format 3 . in this paper, we make the following contributions: (1) we articulate the potential of nlp to enhance sd-at the time of writing this is the first time nlp is systematically applied to this field; ## the stories2insights corpus: a corpus annotated for user-perceived values to enable research in upv classification, we release s2i, a corpus of labelled reports from 7 rural villages in uganda (figure 2c ). in this section, we report on the corpus collection and annotation procedures and outline the challenges this poses for nlp. ## conclusions and future work in this study, we provided a first stepping stone towards future research at the intersection of nlp and sustainable development (sd). as a case study, we investigated the opportunity of nlp to enhancing project sustainability through improved community profiling by providing a cost effective way towards structuring qualitative data. this research is in line with a general call for ai towards social good, where the potential positive impact of nlp is notably missing. in this context, we proposed the new challenging task of automatic user-perceived values classification: we provided the task definition, an annotated dataset (the sto-ries2insights corpus) and a set of light (in terms of overall number of parameters) neural baselines for future reference. future work will investigate ways to improve performance (and especially precision scores) on our data, in particular on low-support labels. possible research direction could include more sophisticated thresholding selection techniques @xcite to replace the simple threshold finetuning which is currently used for simplicity. while deeper and computationally heavier models as @xcite could possibly obtain notable gains in performance on our data, it is the responsibility of the nlp community -especially with regards to social good applicationsto provide solutions which don't penalise countries suffering from access biases (as contexts with low access to computational power), as it is the case of many developing countries. we hope our work will spark interest and open a constructive dialogue between the fields of nlp and sd, and result in new interesting applications. appendix b -co-occurrence matrix of user-perceived values in the s2i corpus. the co-occurrence matrix in figure 6 depicts the inter-relatedness between different t3 labels. the intensity of colour corresponds to the number of samples in the s2i corpus where the given t3 labels co-occur. the analysis of labels co-occurrence can offer valuable insights on commonly associated user-perceived values (upvs, @xcite ): this can be useful to highlight challenges and problems in the considered community, which might not be known to the dwellers themselves. while some correlations are typical and expected, others are related to the specific ugandan context, and might be surprising to those external to the location. for example, economic opportunity, food security and preservation of health appear to frequently co-occur with other t3 labels. note that the lack of employment opportunity, the availability of food and the quality of healthcare services represent endemic problems in the rural context studied in this paper. as they constitute primary concerns for most interviewees, it is therefore unsurprising that they were mentioned frequently in relation to many of the items selected as part of the upv game (section 4). a further illustrative example of the cultural context -in this case rural uganda -is the high concurrence of unburden and mobility. this can be explained by the fact that rural roads are often of poor quality and villages or areas are inaccessible by motorised vehicles. henceforth, people are required to find alternatives moves of transport for moving themselves to hospital or crops to the nearest market for sell, for example. as a final example, the frequent mentioning of faith, harmony and morality, which also tend to co-occur in similar contexts, testifies the fundamental role played by religion in the rural villages considered in this study. the information on the (co-)occurrence of upvs in a community is also particularly valuable for designing appropriate project communication (figure 1b ), which can increase project buy-in through focused messaging (section 3).