SiNFluD: Creating and Evaluating Figurative Language Dataset for Sindhi
Authors: Wazir Ali, Adeeb Noor, Saifullah Tumrani
Organizations: Department of Data Science, Quaid-e-Awam University of Engineering, Science and Technology, Nawabshah, Pakistan. · SoloGenAI Pvt. Ltd. · Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia. · SDAIA-KFUPM Joint Research Center for Artificial Intelligence, King Fahd University of Petroleum & Minerals, Dhahran, Kingdom of Saudi Arabia.
Abstract
In this article, we introduce SiNFluD, a novel benchmark dataset for Sindhi figurative language classification. We first collect raw text from various blogs, social media platforms, and literary sources, and subsequently prepare the corpus for annotation. Two native annotators label the data using the Doccano text annotation tool, achieving an inter-annotator agreement of 0.81. We then establish baseline results using 5-fold and 10-fold cross-validation. Finally, we evaluate mBERT, XLM-RoBERTa, and XLM-RoBERTa-XL models, along with SetFit for few-shot fine-tuning of sentence transformers. Among these, the pretrained XLM-RoBERTa-XL achieves the best performance.