Neural Machine Translation

Machine Translation Weekly 43: Dynamic Programming Encoding

One of the narratives people (including me) love to associate with neural machine translation is that we got rid of all linguistic assumptions about the text and let the neural network learn their own way independent of what people think about language. It sounds cool, it almost gives a science-fiction feeling. What I think that we really do is that we move our assumptions about language from hard constrains of discrete representation into soft constraints of inductive biases that we […]

September 29, 2020 machine translation, NMT

Intuitive explanation of Neural Machine Translation

A simple explanation of Sequence to Sequence model for Neural Machine Translation(NMT) What is Neural Machine Translation?

September 29, 2020 machine translation, NMT

Implementing Neural Machine Translation with Attention mechanism using Tensorflow

A step by step explanation of Tensorflow implementation of neural machine translation(NMT) using Bahdanau’s Attention. In this article, you will learn how to implement sequence to sequence(seq2seq) To finish reading, please visit source site

September 29, 2020 machine translation, NMT

Neural Machine Translation: Using Open-NMT for training a translation model

A complete guide to learn translations between any language pairs

September 29, 2020 machine translation, NMT

Neural Machine Translation with Python

The embedding model’s validation accuracy ends at 0.8401. Model 3: Bidirectional RNNs

September 29, 2020 machine translation, NMT

Neural Machine Translation — Using seq2seq with Keras

Firstly we will go about training the network. Then we will look at the inference models on how to translate a given English sentence to French. Inference model (used for predicting on the input sequence) has a slightly different decoder architecture and we will discuss that in detail when we come there.

September 29, 2020 machine translation, NMT

Word Level English to Marathi Neural Machine Translation using Encoder-Decoder Model

The LSTM reads the data one sequence after the other. Thus if the input is a sequence of length ‘k’, we say that LSTM reads it in ‘k’ time steps (think of this as a for loop with ‘k’ iterations). Referring to the To finish reading, please visit source site

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