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
Examination: 0/2 C
Instructors: Jindřich Helcl, Jindřich Libovický
The course is not taught this semester. Looking forward to see you next year!
1. Introductory notes on machine translation and deep learning Logistics RNNs Reading Questions
2. Sequence-to-sequence learning using Recurrent Neural Networks Encoder-Decoder architecture Reading Questions
Unless otherwise stated, teaching materials for this course are available under CC BY-SA 4.0.
Covered topics: what is MT, deep liearing, embeddings, RNNs, vanishing gradient, LSTM
Reading 1 hour Tao Lei et al., Simple Recurrent Units for Highly Parallelizable Recurrence. EMNLP 2018.
Mar 3 Encoder-Decoder architecture
Covered topics: sequence-to-sequence learning with RNNs, attention
Reading 1 hour Kasai et al., Deep Encoder, Shallow Decoder: Reevaluating Non-autoregressive Machine Translation. ICLR 2021.
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.
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.
There will be a final written test that will not be graded.