Search Results for author: Josef Jon

Found 11 papers, 3 papers with code

Character-level NMT and language similarity

no code implementations8 Aug 2023 Josef Jon, Ondřej Bojar

We explore the effectiveness of character-level neural machine translation using Transformer architecture for various levels of language similarity and size of the training dataset on translation between Czech and Croatian, German, Hungarian, Slovak, and Spanish.

Machine Translation NMT +2

Breeding Machine Translations: Evolutionary approach to survive and thrive in the world of automated evaluation

no code implementations30 May 2023 Josef Jon, Ondřej Bojar

With a combination of multiple MT metrics as the fitness function, the proposed method leads to an increase in translation quality as measured by other held-out automatic metrics.

Machine Translation Translation

CUNI Submission in WMT22 General Task

no code implementations29 Nov 2022 Josef Jon, Martin Popel, Ondřej Bojar

We evaluate performance of MBR decoding compared to traditional mixed backtranslation training and we show a possible synergy when using both of the techniques simultaneously.

Translation

CUNI systems for WMT21: Terminology translation Shared Task

no code implementations WMT (EMNLP) 2021 Josef Jon, Michal Novák, João Paulo Aires, Dušan Variš, Ondřej Bojar

Our approach is based on providing the desired translations alongside the input sentence and training the model to use these provided terms.

Sentence Translation

End-to-End Lexically Constrained Machine Translation for Morphologically Rich Languages

no code implementations ACL 2021 Josef Jon, João Paulo Aires, Dušan Variš, Ondřej Bojar

Lexically constrained machine translation allows the user to manipulate the output sentence by enforcing the presence or absence of certain words and phrases.

Machine Translation Sentence +1

JokeMeter at SemEval-2020 Task 7: Convolutional humor

no code implementations SEMEVAL 2020 Martin Docekal, Martin Fajcik, Josef Jon, Pavel Smrz

This paper describes our system that was designed for Humor evaluation within the SemEval-2020 Task 7.

BUT-FIT at SemEval-2020 Task 4: Multilingual commonsense

1 code implementation SEMEVAL 2020 Josef Jon, Martin Fajčík, Martin Dočekal, Pavel Smrž

We show that with a strong machine translation system, our system can be used in another language with a small accuracy loss.

Data Augmentation Machine Translation +1

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