Improved Bangla Language Modeling with Convolution
Shuvendu Roy · 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT) · 2019
Language modeling is a fundamental task for building any natural language processing application or language understandable intelligent system. Language modeling becomes difficult with the increase of input sequence length. It is even more difficult for Bangla as it is more complex than many other languages and contains more character in its alphabet. Few previous works that were done on Bangla language modeling used statistical language modeling or simple neural network very recently. With the neural network, the RNN is the fundamental choice to deal with sequential data but we follow the recent development in convolutional neural network in sequential modeling. In this work, we used dilated convolution to solve the issues of long sequence and improved the performance of Bangla language modeling.