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Improving Cross-Lingual CSR Classification Using Pretrained Transformers with Variable Selection Networks and Data Augmentation

This paper describes our submission to the Cross-Lingual Classification of Corporate Social Responsibility (CSR) Themes and Topics shared task, aiming to identify themes and fine-grained topics present in news articles. Classifying news articles poses several challenges, including limited training data, noisy articles, and longer context length. In this paper, we explore the potential of using pretrained transformer models to classify news articles into CSR themes and fine-grained topics. We propose two different approaches for these tasks. For multi-class classification of CSR themes, we suggest using a pretrained multi-lingual encoder-based model like microsoft/mDeBERTa-v3-base, along with a variable selection network to classify the article into CSR themes. To identify all fine-grained topics in each article, we propose using a pretrained encoder-based model like Longformer, which offers a higher context length. We employ chunking-based inference to avoid information loss in inference and experimented with using different parts and manifestation of original article for training and inference.