Neural Machine Translation: Seq2Seq Model with Attention, MBR Decode in TensorFlow – Streamlit Demo

Neural Machine Translation (NMT) is a powerful approach to machine translation that has gained popularity in recent years. One popular NMT model is the Sequence-to-Sequence (Seq2Seq) model with attention, which has been shown to achieve state-of-the-art performance in translation tasks.

TensorFlow is a popular open-source machine learning framework that provides tools for building and training NMT models. In this demo, we will showcase a Seq2Seq model with attention and Minimum Bayes-Risk (MBR) decoding using TensorFlow and Streamlit.

What is Seq2Seq Model with Attention?

The Seq2Seq model with attention is a type of NMT model that is designed to handle variable-length input and output sequences. It consists of an encoder and a decoder, where the encoder processes the input sequence and produces a fixed-size representation (often called the context vector), and the decoder generates the output sequence based on this context vector.

Attention mechanisms are used to allow the decoder to focus on different parts of the input sequence at each step of generation, which has been shown to improve the accuracy of the model in translation tasks.

MBR Decode in TensorFlow

MBR decoding is a post-processing step that can be applied to the output of the Seq2Seq model to further improve translation quality. It involves re-ranking the N-best translation hypotheses based on some measure of translation quality, such as BLEU score or TER. This can help to mitigate errors introduced by the model and produce more fluent and accurate translations.

Streamlit Demo

Streamlit is an open-source Python library that allows you to create interactive web applications for machine learning and data science. In this demo, we will use Streamlit to showcase a live demo of the Seq2Seq model with attention and MBR decoding in TensorFlow. Users will be able to input a source text in one language and see the model’s translation output in another language, along with the MBR re-ranked translations.

This demo will provide a hands-on experience of how NMT models can be used for machine translation and demonstrate the capabilities of Seq2Seq models with attention and MBR decoding in TensorFlow.