# English to Bangla Machine Translation Using Recurrent Neural Network

arXiv: https://arxiv.org/abs/2106.07225 · HTML: https://mlsanity.com/paper/2106.07225/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 18 references that are in the map.
- First posted on arXiv 14 Jun 2021.

## Builds on (most cited first, 5 of 5)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [Gate-Variants of Gated Recurrent Unit (GRU) Neural Networks](https://mlsanity.com/paper/1701.05923/) (2017)
- [Improved Recurrent Neural Networks for Session-based Recommendations](https://mlsanity.com/paper/1606.08117/) (2016)

## Built on by (most cited first, 6 of 6)

- [Vashantor: A Large-scale Multilingual Benchmark Dataset for Automated Translation of Bangla Regional Dialects to Bangla Language](https://mlsanity.com/paper/2311.11142/) (2023)
- [All You Need In Sign Language Production](https://mlsanity.com/paper/2201.01609/) (2022)
- [Bridging Dialects: Translating Standard Bangla to Regional Variants Using Neural Models](https://mlsanity.com/paper/2501.05749/) (2025)
- [BanglaDialecto: An End-to-End AI-Powered Regional Speech Standardization](https://mlsanity.com/paper/2411.10879/) (2024)
- [Transformer-Based Low-Resource Language Translation: A Study on Standard Bengali to Sylheti](https://mlsanity.com/paper/2510.18898/) (2025)
- [Improving Bangla Linguistics: Advanced LSTM, Bi-LSTM, and Seq2Seq Models for Translating Sylheti to Modern Bangla](https://mlsanity.com/paper/2505.18709/) (2025)

## Abstract (arXiv)

The applications of recurrent neural networks in machine translation are increasing in natural language processing. Besides other languages, Bangla language contains a large amount of vocabulary. Improvement of English to Bangla machine translation would be a significant contribution to Bangla Language processing. This paper describes an architecture of English to Bangla machine translation system. The system has been implemented with the encoder-decoder recurrent neural network. The model uses a knowledge-based context vector for the mapping of English and Bangla words. Performances of the model based on activation functions are measured here. The best performance is achieved for the linear activation function in encoder layer and the tanh activation function in decoder layer. From the execution of GRU and LSTM layer, GRU performed better than LSTM. The attention layers are enacted with softmax and sigmoid activation function. The approach of the model outperforms the previous state-of-the-art systems in terms of cross-entropy loss metrics. The reader can easily find out the structure of the machine translation of English to Bangla and the efficient activation functions from the paper.