NPFL116 – Compendium of Neural Machine Translation

This seminar should make the students familiar with the current research trends in machine translation using deep neural networks. The students should most importantly learn how to deal with the ever-growing body of literature on empirical research in machine translation and critically asses its content. The semester consists of few lectures summarizing the state of the art, discussions on reading assignments and student presentation of selected papers.


SIS code: NPFL116
Semester: summer
E-credits: 3
Examination: 0/2 C
Instructors: Jindřich Helcl, Jindřich Libovický (remotely)

Timespace Coordinates

The course is held on Wednesdays at 14:00 in S1. The first lecture is on February 26.


1. Introductory notes on machine translation and deep learning Logistics NN Intro Reading Questions

2. Sequence-to-sequence learning using Recurrent Neural Networks Sequence-to-Sequence Reading

1. Introductory notes on machine translation and deep learning

 Feb 20 Logistics NN Intro


Reading  1.5 hour LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. Deep learning. Nature 521.7553 (2015): 436.


  • Can you identify some implicit assumptions the authors make about sentence meaning while talking about NMT?
  • Do you think they are correct?
  • How do the properties that the authors attribute to LSTM networks correspond to your own ideas how should language be computationally processed?

2. Sequence-to-sequence learning using Recurrent Neural Networks

 Mar 4 Sequence-to-Sequence

Covered topics: embeddings, RNNs, vanishing gradient, LSTM, encoder-decoder, attention

Reading  2 hours

Vaswani, Ashish, et al. Attention is all you need. Advances in Neural Information Processing Systems. 2017.

Reading assignments

There will be a reading assignment after every class. You will be given few question about the reading that you should submit before the next lecture.

Student presentations

Students will present one of the selected groups of papers to their fellow students. The presenting students will also prepare questions for discussion after the paper presentation.

Others should also get familiar with the paper so they can participate in the discussion.

It is strongly encouraged to arrange a consultation with the course instructors at least one day before the presentation.

Final written test

There will be a final written test that will not be graded.