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Though there has been a large body of recent works in language modeling (LM)for high resource languages such as English and Chinese, the area is stillunexplored for low resource languages like Bengali and Hindi. We propose an endto end trainable memory efficient CNN architecture named CoCNN to handlespecific characteristics such as high inflection, morphological richness,flexible word order and phonetical spelling errors of Bengali and Hindi. Inparticular, we introduce two learnable convolutional sub-models at word and atsentence level that are end to end trainable. We show that state-of-the-art(SOTA) Transformer models including pretrained BERT do not necessarily yieldthe best performance for Bengali and Hindi. CoCNN outperforms pretrained BERTwith 16X less parameters, and it achieves much better performance than SOTALSTM models on multiple real-world datasets. This is the first study on theeffectiveness of different architectures drawn from three deep learningparadigms - Convolution, Recurrent, and Transformer neural nets for modelingtwo widely used languages, Bengali and Hindi.

Paradigm Shift in Language Modeling: Revisiting CNN for Modeling Sanskrit Originated Bengali and Hindi Language