Deep Learning for Machine Translation

Lecturers: Hassan Sajjad and Fahim Dalvi


In this lecture series, we first cover the basics of statistical machine translation to establish the intuition behind machine translation. We then cover the basics of neural network models - word embedding and neural language model. Finally, we learn an end-to-end translation system based completely on deep neural networks. In the last part of the lecture series, we learn to peek into these neural systems and analyze what they learn about the intricacies of a language like morphology and syntax, without ever explicitly seeing these details in the training data.


Background reading

Slides


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