|
|
|
||
|
Participants will get acquainted with methods of machine translation that rely on automatic processing of (large) training data as well as with open-source implementations of these methods. We will cover a range of approaches organized along two axes: the level of linguistic analysis (uninformed, utilizing
morphology, surface and deep syntax) and the depth of machine learning methods (classical statistical MT that decomposes input into pieces and neural MT that models the task end to end; a particular focus is given to the Transformer model which forms the basis of the current large models).
Last update: Mírovský Jiří, RNDr., Ph.D. (23.05.2025)
|
|
||
|
The goal is to provide (1) a big overview of successful approaches to MT since 1990, including the recent developments due to deep learning after 2015 and due to large language models after 2022, and (2) detailed technical knowledge and practical experience with one of the approaches or some MT-related tool according to the student's choice. The second goal often leads to the publication of the student's work at a relevant workshop. Last update: Mírovský Jiří, RNDr., Ph.D. (23.05.2025)
|
|
||
|
Key requirements:
Work on a project (alone or in a group of two to three). Present project results (~30-minute talk). Write a report (~4-page scientific paper).
Contributions to the grade:
10% homework and activity, 30% written exam, 50% project report, 10% project presentation.
The 'credit' (zapocet) is given based on the continuous work on the project throughout the semester. The 'credit' is not required prior to the written exam.
Final Grade: ≥50% good, ≥70% very good, ≥90% excellent.
Last update: Bojar Ondřej, doc. RNDr., Ph.D. (17.06.2019)
|
|
||
|