Machine Translation

Explore the fundamentals of machine translation, tracing its history from rule-based and statistical methods to modern neural machine translation (NMT). Understand how NMT systems work and their advantages over earlier approaches. This lesson prepares you to build and evaluate neural machine translation models using sequence-to-sequence learning.

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What is machine translation (MT)?
A brief historical tour of MT

What is machine translation (MT)?

Humans often communicate with each other by means of a language, compared to other communication methods (for example, gesturing). Currently, more than 6,000 languages are spoken worldwide. Furthermore, learning a language to a level where it’s easily understandable to a native speaker of that language is a difficult task to master. However, communication is essential for sharing knowledge, socializing, and expanding our network. Therefore, language acts as a barrier to communicating with people in different parts of the world. This is where machine translation (MT) comes in. MT systems allow the user to input a sentence in their own language (known as the source language) and output a sentence in a desired target language.

The problem with MT can be formulated as follows. Say we’re given a sentence (or a sequence of words) $W_s$ belonging to a source language $S$ , defined by the following:

1.Introduction to Natural Language Processing

2.Understanding TensorFlow 2

3.Word2vec: Learning Word Embeddings

4. Advanced Word Vector Algorithms

5.Sentence Classification with Convolutional Neural Networks

6.Recurrent Neural Networks

7.Understanding Long Short-Term Memory Networks

8.Applications of LSTM: Generating Text

9.Sequence-to-Sequence Learning: Neural Machine Translation

10.Transformers

Project

11.Image Captioning with Transformers

12.Final Remarks

13.Appendix: Mathematical Foundations and Advanced TensorFlow

Mock Interview

Machine Translation

What is machine translation (MT)?