🚨 Exciting News! A great achievement for CCDS!!!!
We are thrilled to announce that two of our research papers — BanMime and DM-Codec — have been accepted at EMNLP 2025, one of the most prestigious conferences in NLP (Core Rank A*).
📌 BanMime has been accepted into the EMNLP 2025 Main Conference 🎉
📌 DM-Codec has been accepted into the EMNLP 2025 Findings 🎉
🖼️ BanMime: A Novel Bangla Meme Dataset for Visual Metaphor Explanation
In this work, we introduce BanMime, the first-ever dataset for Bangla memes enriched with human-annotated explanations for metaphors.
🧩 We curated a dataset of 2,000 misogynistic memes in Bangla. Each meme was labeled, and based on those labels, explanations were annotated to uncover the underlying metaphor.
🔍 Our experiments reveal that existing open-source Vision-Language Models (VLMs) struggle to comprehend visual metaphors, and their performance is even worse when generating explanations.
This work is a joint collaboration between CUET, DIU, and CCDS.
✍️ Authors: Md Ayon Mia*, Akm Moshiur Rahman Mazumder*, Khadiza Sultana Sayma, Md Fahim, Md Tahmid Hasan Fuad, MUHAMMAD IBRAHIM KHAN, AKM MAHBUBUR RAHMAN
(*Equal Contribution)
🖼️ DM-Codec: Distilling Multimodal Representations for Speech Tokenization
DM-Codec is a novel speech tokenizer that integrates multimodal (acoustic, semantic, and contextual) representations via a language model (LM) and self-supervised speech model (SM) through distillation.
🔍 DM-Codec outperforms state-of-the-art models, reducing Word Error Rate (WER) by up to 13.46% and enhancing speech intelligibility and quality on the LibriSpeech dataset.
This work is done in collaboration with AWS (GenAI), Qatar Computing Research Institute, and University of Virginia
✍️ Authors: Md Mubtasim Ahasan, Md Fahim, Tasnim Mohiuddin, A K M Mahbubur Rahman, Aman Chadha, Tariq Iqbal, M Ashraful Amin, Md Mofijul Islam, Amin Ahsan Ali
Congratulations to the all co-authors, collaborators and supervisors 🎊🎊