A RoBERTa Approach for Automated Processing of Sustainability Reports

Merih Angin, Beyza Taşdemir, Cenk Arda Yılmaz, Gökcan Demiralp, Mert Atay, Pelin Angin () and Gökhan Dikmener
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Merih Angin: Department of International Relations, Koc University, Istanbul 34450, Turkey
Beyza Taşdemir: Department of Computer Engineering, Middle East Technical University, Ankara 06800, Turkey
Cenk Arda Yılmaz: Department of Computer Engineering, Middle East Technical University, Ankara 06800, Turkey
Gökcan Demiralp: Department of Computer Engineering, Middle East Technical University, Ankara 06800, Turkey
Mert Atay: Department of Computer Engineering, Middle East Technical University, Ankara 06800, Turkey
Pelin Angin: Department of Computer Engineering, Middle East Technical University, Ankara 06800, Turkey
Gökhan Dikmener: United Nations Development Programme, SDG AI Lab, Istanbul 34381, Turkey

Sustainability, 2022, vol. 14, issue 23, 1-25

Abstract: There is a strong need and demand from the United Nations, public institutions, and the private sector for classifying government publications, policy briefs, academic literature, and corporate social responsibility reports according to their relevance to the Sustainable Development Goals (SDGs). It is well understood that the SDGs play a major role in the strategic objectives of various entities. However, linking projects and activities to the SDGs has not always been straightforward or possible with existing methodologies. Natural language processing (NLP) techniques offer a new avenue to identify linkages for SDGs from text data. This research examines various machine learning approaches optimized for NLP-based text classification tasks for their success in classifying reports according to their relevance to the SDGs. Extensive experiments have been performed with the recently released Open Source SDG (OSDG) Community Dataset, which contains texts with their related SDG label as validated by community volunteers. Results demonstrate that especially fine-tuned RoBERTa achieves very high performance in the attempted task, which is promising for automated processing of large collections of sustainability reports for detection of relevance to SDGs.

Keywords: corporate social responsibility; natural language processing; RoBERTa; sustainable development goals (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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