no code implementations • 8 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.
no code implementations • 7 Aug 2023 • Josef Jon, Dušan Variš, Michal Novák, João Paulo Aires, Ondřej Bojar
This paper explores negative lexical constraining in English to Czech neural machine translation.
no code implementations • 30 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.
no code implementations • 29 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.
no code implementations • WMT (EMNLP) 2021 • Josef Jon, Michal Novák, João Paulo Aires, Dušan Variš, Ondřej Bojar
This paper describes Charles University submission for Multilingual Low-Resource Translation for Indo-European Languages shared task at WMT21.
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.
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.
1 code implementation • EMNLP (MRQA) 2021 • Martin Fajcik, Josef Jon, Pavel Smrz
Therefore we propose multiple approaches to modelling joint probability $P(a_s, a_e)$ directly.
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.
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.
1 code implementation • SEMEVAL 2020 • Martin Fajcik, Josef Jon, Martin Docekal, Pavel Smrz
This paper describes BUT-FIT's submission at SemEval-2020 Task 5: Modelling Causal Reasoning in Language: Detecting Counterfactuals.