1 code implementation • RANLP 2021 • Michal Auersperger, Pavel Pecina
We address the compositionality challenge presented by the SCAN benchmark.
no code implementations • WMT (EMNLP) 2021 • Mateusz Krubiński, Erfan Ghadery, Marie-Francine Moens, Pavel Pecina
In this paper, we describe our submission to the WMT 2021 Metrics Shared Task.
1 code implementation • WMT (EMNLP) 2021 • Mateusz Krubiński, Erfan Ghadery, Marie-Francine Moens, Pavel Pecina
In this paper, we show that automatically-generated questions and answers can be used to evaluate the quality of Machine Translation (MT) systems.
no code implementations • NAACL (ACL) 2022 • Michal Auersperger, Pavel Pecina
Compositionality has traditionally been understood as a major factor in productivity of language and, more broadly, human cognition.
no code implementations • ACL 2020 • Shadi Saleh, Pavel Pecina
We exploit the Statistical Machine Translation (SMT) and Neural Machine Translation (NMT) paradigms and train several domain-specific and task-specific machine translation systems to translate the non-English queries into English (for the QT approach) and the English documents to all the query languages (for the DT approach).
no code implementations • WS 2019 • Martin Popel, Dominik Macháček, Michal Auersperger, Ondřej Bojar, Pavel Pecina
We describe our NMT systems submitted to the WMT19 shared task in English-Czech news translation.
no code implementations • WS 2017 • Antonio Jimeno Yepes, Aur{\'e}lie N{\'e}v{\'e}ol, Mariana Neves, Karin Verspoor, Ond{\v{r}}ej Bojar, Arthur Boyer, Cristian Grozea, Barry Haddow, Madeleine Kittner, Yvonne Lichtblau, Pavel Pecina, Rol Roller, , Rudolf Rosa, Amy Siu, Philippe Thomas, Saskia Trescher
no code implementations • 5 Aug 2017 • Jan Hajič jr., Pavel Pecina
Noteheads are the interface between the written score and music.
1 code implementation • 14 Mar 2017 • Jan Hajič jr., Pavel Pecina
Optical Music Recognition (OMR) has long been without an adequate dataset and ground truth for evaluating OMR systems, which has been a major problem for establishing a state of the art in the field.
no code implementations • WS 2016 • Ond{\v{r}}ej Bojar, Christian Buck, Rajen Chatterjee, Christian Federmann, Liane Guillou, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Aur{\'e}lie N{\'e}v{\'e}ol, Mariana Neves, Pavel Pecina, Martin Popel, Philipp Koehn, Christof Monz, Matteo Negri, Matt Post, Lucia Specia, Karin Verspoor, J{\"o}rg Tiedemann, Marco Turchi
no code implementations • WS 2016 • Jindřich Libovický, Jindřich Helcl, Marek Tlustý, Pavel Pecina, Ondřej Bojar
Neural sequence to sequence learning recently became a very promising paradigm in machine translation, achieving competitive results with statistical phrase-based systems.
no code implementations • WS 2014 • Ondrej Bojar, Christian Buck, Christian Federmann, Barry Haddow, Philipp Koehn, Johannes Leveling, Christof Monz, Pavel Pecina, Matt Post, Herve Saint-Amand, Radu Soricut, Lucia Specia, Aleš Tamchyna
no code implementations • LREC 2014 • Zde{\v{n}}ka Ure{\v{s}}ov{\'a}, Jan Haji{\v{c}}, Pavel Pecina, Ond{\v{r}}ej Du{\v{s}}ek
This paper presents development and test sets for machine translation of search queries in cross-lingual information retrieval in the medical domain.
no code implementations • LREC 2012 • Khaled Shaalan, Mohammed Attia, Pavel Pecina, Younes Samih, Josef van Genabith
Furthermore, from a large list of valid forms and invalid forms we create a character-based tri-gram language model to approximate knowledge about permissible character clusters in Arabic, creating a novel method for detecting spelling errors.
no code implementations • LREC 2012 • Eleftherios Avramidis, Marta R. Costa-juss{\`a}, Christian Federmann, Josef van Genabith, Maite Melero, Pavel Pecina
This corpus aims to serve as a basic resource for further research on whether hybrid machine translation algorithms and system combination techniques can benefit from additional (linguistically motivated, decoding, and runtime) information provided by the different systems involved.
no code implementations • LREC 2012 • Christian Federmann, Eleftherios Avramidis, Marta R. Costa-juss{\`a}, Josef van Genabith, Maite Melero, Pavel Pecina
We describe the Shared Task on Applying Machine Learning Techniques to Optimise the Division of Labour in Hybrid Machine Translation (ML4HMT) which aims to foster research on improved system combination approaches for machine translation (MT).