SiNFluD: A novel benchmark dataset for sindhi figurative language classification
Year: 2026
Type: data-paper
Abstract: In this article, we introduce SiNFluD, a novel benchmark dataset for Sindhi figurative language classification. We collect phrases from web raw text sourced 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 (Cohen's Kappa). 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 the SetFit experiments, we exploited the sentence Transformer as the backbone encoder. This multilingual MPNet checkpoint provides cross-lingual sentence embeddings and was fine-tuned using SetFit's parameter-efficient contrastive learning approach, followed by training a lightweight classification head on the Sindhi few-shot dataset. Among these, the pretrained XLM-RoBERTa-XL achieves the best classification performance with an accuracy of 92.27%. The SiNFluD dataset, comprising 4,451 annotated instances across five categories (literal, idiom, proverb, metaphor, and simile), provides a foundational resource for figurative language research in Sindhi and other low-resource South Asian languages.
Source: Data in Brief
Authors: Wazir Ali, Adeeb Noor, Saifullah Tumrani
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,220
Funder: King Abdulaziz University
OpenAlex ID: W7220835027