Data Innovation for International Development: An overview of natural language processing for qualitative data analysis

Availability, collection and access to quantitative data, as well as its limitations, often make qualitative data the resource upon which development programs heavily rely. Both traditional interview data and social media analysis can provide rich contextual information and are essential for research, appraisal, monitoring and evaluation. These data may be difficult to process and analyze both systematically and at scale. This, in turn, limits the ability of timely data driven decision-making which is essential in fast evolving complex social systems. In this paper, we discuss the potential of using natural language processing to systematize analysis of qualitative data, and to inform quick decision-making in the development context. We illustrate this with interview data generated in a format of micro-narratives for the UNDP Fragments of Impact project

Machine translation as an underrated ingredient? : solving classification tasks with large language models for comparative research

While large language models have revolutionised computational text analysis methods, the field is still tilted towards English language resources. Even as there are pre-trained models for some "smaller" languages, the coverage is far from universal, and pre-training large language models is an expensive and complicated task. This uneven language coverage limits comparative social research in terms of its geographical and linguistic scope. We propose a solution that sidesteps these issues by leveraging transfer learning and open-source machine translation. We use English as a bridge language between Hungarian and Polish bills and laws to solve a classification task related to the Comparative Agendas Project (CAP) coding scheme. Using the Hungarian corpus as training data for model fine-tuning, we categorise the Polish laws into 20 CAP categories. In doing so, we compare the performance of Transformer-based deep learning models (monolinguals, such as BERT, and multilinguals such as XLM-RoBERTa) and machine learning algorithms (e.g., SVM). Results show that the fine-tuned large language models outperform the traditional supervised learning benchmarks but are themselves surpassed by the machine translation approach. Overall, the proposed solution demonstrates a viable option for applying a transfer learning framework for low-resource languages and achieving state-of-the-art results without requiring expensive pre-training

Improving the translation environment for professional translators

When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side. This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project

Visual Affect Around the World: A Large-scale Multilingual Visual Sentiment Ontology

Every culture and language is unique. Our work expressly focuses on the uniqueness of culture and language in relation to human affect, specifically sentiment and emotion semantics, and how they manifest in social multimedia. We develop sets of sentiment- and emotion-polarized visual concepts by adapting semantic structures called adjective-noun pairs, originally introduced by Borth et al. (2013), but in a multilingual context. We propose a new language-dependent method for automatic discovery of these adjective-noun constructs. We show how this pipeline can be applied on a social multimedia platform for the creation of a large-scale multilingual visual sentiment concept ontology (MVSO). Unlike the flat structure in Borth et al. (2013), our unified ontology is organized hierarchically by multilingual clusters of visually detectable nouns and subclusters of emotionally biased versions of these nouns. In addition, we present an image-based prediction task to show how generalizable language-specific models are in a multilingual context. A new, publicly available dataset of >15.6K sentiment-biased visual concepts across 12 languages with language-specific detector banks, >7.36M images and their metadata is also released.Comment: 11 pages, to appear at ACM MM'1