Papers by Pavel Straňák
Bridging the LAPPS Grid and CLARIN (L18-1)
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Erhard Hinrichs, Nancy Ide, James Pustejovsky, Jan Hajič, Marie Hinrichs, Mohammad Fazleh Elahi, Keith Suderman, Marc Verhagen, Kyeongmin Rim, Pavel Straňák, Jozef Mišutka
| Challenge: | The LAPPS-CLARIN project is creating a "trust network" between the Language Applications Grid and WebLicht workflow engine . the goal is to allow users on one side of the bridge to gain appropriately authenticated access to the other . |
| Approach: | The LAPPS-CLARIN project is creating a "trust network" between the Language Applications Grid and WebLicht workflow engine hosted by the CLARIN-D Center in Tübingen. |
| Outcome: | The LAPPS-CLARIN project is creating a "trust network" between the Language Applications (LAPPS) Grid and the WebLicht workflow engine hosted by the CLARIN-D Center in Tübingen. |
Charles Translator: A Machine Translation System between Ukrainian and Czech (2024.lrec-main)
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Martin Popel, Lucie Polakova, Michal Novák, Jindřich Helcl, Jindřich Libovický, Pavel Straňák, Tomas Krabac, Jaroslava Hlavacova, Mariia Anisimova, Tereza Chlanova
| Challenge: | a system for translating between Ukrainian and Czech was developed in the spring of 2022 . the system was not available at the time in the required quality . |
| Approach: | They propose a machine translation system between Ukrainian and Czech to reduce the impact of the Russian-Ukrainian war on individuals and society. |
| Outcome: | The proposed system translates directly between Ukrainian and Czech, compared to other systems that use English as a pivot. |
Diacritics Restoration Using Neural Networks (L18-1)
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| Challenge: | a novel combination of character-level recurrent neural network and language model is proposed . people often replace characters with diacritics with their ASCII counterparts . |
| Approach: | They propose a character-level recurrent neural network-based model and a language model for diacritics restoration. |
| Outcome: | The proposed model reduces error of current best systems by 20% to 64% on four languages . it is also able to restore diacritical marks on a number of languages using the same model . |