Research Topic: Efficient Dialect-Aware Bangla Embedding Model using SSMs We're starting a new research proje…

By Shah Nawaz Haider

Research Topic: Efficient Dialect-Aware Bangla Embedding Model using SSMs

We're starting a new research project to develop efficient dialect-aware embedding models for Bangla.

What will we build?

Our goal is to design an embedding model that learns rich semantic representations while understanding regional dialects. Instead of relying solely on Transformer-based architectures, we'll explore State Space Models (SSMs) to create embeddings that are faster, more memory-efficient, and scalable.

Research Objectives

- To build a dialect-aware text embedding model that captures semantic similarity across different dialects.

- Explore State Space Models (SSMs) as an efficient alternative to Transformers.

- Evaluate on tasks such as semantic search, retrieval, text classification, clustering, and RAG.

Why is this important?

Most existing embedding models are trained primarily on standard languages and often fail to capture the nuances of regional dialects. A lightweight, dialect-aware embedding model can significantly improve downstream NLP systems while reducing computational costs, making advanced AI more accessible for low-resource languages such as Bangla and its dialects.

Potential Applications

- Semantic Search

- Retrieval-Augmented Generation (RAG)

- Chatbots and Conversational AI

- Machine Translation

- Text Classification

- Document Retrieval

- Speech and Multimodal Applications

We're looking for members interested in NLP, embedding models, multilingual AI, State Space Models, and low-resource language research. If you'd like to contribute to cutting-edge research with real-world impact, join us!