Creating and Evaluating Figurative Language Dataset for Sindhi
This work provides the first benchmark dataset for figurative language in Sindhi, enabling future research in this low-resource language.
The paper introduces SiNFluD, a new benchmark dataset for Sindhi figurative language classification, and evaluates several multilingual models, with XLM-RoBERTa-XL achieving the best performance.
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.