Papers by Mohammad Rafsan
CNN for Modeling Sanskrit Originated Bengali and Hindi Language (2022.aacl-main)
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Chowdhury Rahman, MD. Hasibur Rahman, Mohammad Rafsan, Mohammed Eunus Ali, Samiha Zakir, Rafsanjani Muhammod
| Challenge: | Existing studies on the effectiveness of different architectures for modeling low resource languages are limited. |
| Approach: | They propose a trainable memory efficient CNN architecture for Bengali and Hindi . they propose two learnable convolutional sub-models that are end to end trainable . |
| Outcome: | The proposed model outperforms pretrained BERT models on Bengali and Hindi with 16X less parameters and achieves much better performance than SOTA LSTMs on multiple real-world datasets. |