Search Results for author: Pavel Pecina

Found 25 papers, 3 papers with code

Document Translation vs. Query Translation for Cross-Lingual Information Retrieval in the Medical Domain

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).

Document Translation Information Retrieval +2

In Search of a Dataset for Handwritten Optical Music Recognition: Introducing MUSCIMA++

1 code implementation14 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.

CUNI System for WMT16 Automatic Post-Editing and Multimodal Translation Tasks

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.

Automatic Post-Editing Multimodal Machine Translation +1

The ML4HMT Workshop on Optimising the Division of Labour in Hybrid Machine Translation

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).

Language Modelling Machine Translation +1

A Richly Annotated, Multilingual Parallel Corpus for Hybrid Machine Translation

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.

Machine Translation Translation

Arabic Word Generation and Modelling for Spell Checking

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.

Language Modelling Morphological Analysis +1

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