Thesis (Selection of subject)Thesis (Selection of subject)(version: 390)
Thesis details
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Metody adaptace neuronového strojového překladu
Thesis title in Czech: Metody adaptace neuronového strojového překladu
Thesis title in English: Adaptation methods of Neural Machine Translation
Key words: hluboké neuronové sítě, neuronový strojový překlad
English key words: deep neural networks, neural machine translation
Academic year of topic announcement: 2020/2021
Thesis type: dissertation
Thesis language:
Department: Institute of Formal and Applied Linguistics (32-UFAL)
Supervisor: doc. RNDr. Pavel Pecina, Ph.D.
Author: hidden - assigned and confirmed by the Study Dept.
Date of registration: 08.09.2020
Date of assignment: 08.09.2020
Confirmed by Study dept. on: 30.09.2020
Guidelines
Neural methods have become the state-of-the-art in the field of machine translation and brought about significant improvements of translation quality.translation. This thesis will focus on methods for adaptation of neural machine translation to specific languages (e.g. dialects) and/or domains) for which there are no sufficient amounts of parallel training data available.
References
Goodfellow, I., Y. Bengio, and A. Courville 2016. Deep learning. Cambridge, MA, USA: MIT press.

Orhan Firat, Kyunghyun Cho, and Yoshua Bengio. Multi-way, multilingual neural machine translation with a shared attention mechanism. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 866–875, San Diego, California, June 2016. Association for Computational Linguistics.

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is All you Need. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems 30, pages 6000–6010. Curran Associates, Inc., 2017.
 
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