4 citations · 6 across the 4 of their papers we have counts for
9 papers
CUNI Systems for the WMT22 Czech-Ukrainian Translation Task
Martin Popel, Jindřich Libovický, Jindřich Helcl
We present Charles University submissions to the WMT22 General Translation Shared Task on Czech-Ukrainian and Ukrainian-Czech machine translation. We present two constrained submis…
Non-Autoregressive Machine Translation: It's Not as Fast as it Seems
Jindřich Helcl, Barry Haddow, Alexandra Birch
Efficient machine translation models are commercially important as they can increase inference speeds, and reduce costs and carbon emissions. Recently, there has been much interest…
CUNI System for the WMT19 Robustness Task
Jindřich Helcl, Jindřich Libovický, Martin Popel
We present our submission to the WMT19 Robustness Task. Our baseline system is the Charles University (CUNI) Transformer system trained for the WMT18 shared task on News Translatio…
End-to-End Non-Autoregressive Neural Machine Translation with Connectionist Temporal Classification
Jindřich Libovický, Jindřich Helcl
Autoregressive decoding is the only part of sequence-to-sequence models that prevents them from massive parallelization at inference time. Non-autoregressive models enable the deco…
Input Combination Strategies for Multi-Source Transformer Decoder
Jindřich Libovický, Jindřich Helcl, David Mareček
In multi-source sequence-to-sequence tasks, the attention mechanism can be modeled in several ways. This topic has been thoroughly studied on recurrent architectures. In this paper…
CUNI System for the WMT18 Multimodal Translation Task
Jindřich Helcl, Jindřich Libovický, Dušan Variš
We present our submission to the WMT18 Multimodal Translation Task. The main feature of our submission is applying a self-attentive network instead of a recurrent neural network. W…