Transformer-Driven Triple Fusion Framework for Enhanced Multimodal Author Intent Classification in Low-Resource Bangla
Published:Nov 28, 2025 15:44
•1 min read
•ArXiv
Analysis
This research focuses on improving author intent classification in the Bangla language, which is considered a low-resource language. The use of a Transformer-based model and a triple fusion framework suggests an attempt to effectively integrate multiple data modalities (e.g., text, images, audio) to improve classification accuracy. The focus on low-resource settings is significant, as it addresses the challenge of limited training data. The paper likely explores the architecture of the fusion framework and evaluates its performance against existing methods.
Key Takeaways
- •Focus on author intent classification.
- •Addresses the challenge of low-resource language (Bangla).
- •Employs a Transformer-based model.
- •Utilizes a triple fusion framework for multimodal data.
- •Aims to improve classification accuracy in a low-resource setting.
Reference
“The research likely explores the architecture of the fusion framework and evaluates its performance against existing methods.”