/Neural-Machine-Translation

This GitHub repository houses an innovative implementation of Neural Machine Translation (NMT) using state-of-the-art sequence-to-sequence networks. The primary focus is on enhancing translation quality through progressively advanced architectural improvements.

Primary LanguageJupyter Notebook

Neural-Machine-Translation

  1. Constructed and deployed a Sequence-to-Sequence network using an encoding and decoding paradigm, utilizing LSTM nodes as the foundation. Implementation achieved a noteworthy test Bleu score of 0.365.

  2. Executed a bidirectional Sequence-to-Sequence network, incorporating both forward and backward encoding. Implementation, succeeded in a commendable test Bleu score of 0.369, highlighting the model’s proficiency in generating high-quality sequences.

  3. Performed a Sequence-to-Sequence network with bidirectional encoding and incorporated an Attention mechanism in the decoding process. This advanced architecture led to remarkable results, attaining a notable test Bleu score of 0.406. The model’s ability to generate accurate and coherent sequences was improved by the inclusion of Attention.