Sentence-parallel corpus made from English and Czech Wikipedias based on translated articles from English into Czech.
The work done is described in the paper: ŠTROMAJEROVÁ, Adéla, Vít BAISA a Marek BLAHUŠ. Between Comparable and Parallel: English-Czech Corpus from Wikipedia. In RASLAN 2016 Recent Advances in Slavonic Natural Language Processing. Brno: Tribun EU, 2016. s. 3-8, 6 s. ISBN 978-80-263-1095-2.
This package contains the eye-tracker recordings of 8 subjects evaluating English-to-Czech machine translation quality using the WMT-style ranking of sentences.
We provide the set of sentences evaluated, the exact screens presented to the annotators (including bounding box information for every area of interest and even for individual letters in the text) and finally the raw EyeLink II files with gaze trajectories.
The description of the experiment can be found in the paper:
Ondřej Bojar, Filip Děchtěrenko, Maria Zelenina. A Pilot Eye-Tracking Study of WMT-Style Ranking Evaluation.
Proceedings of the LREC 2016 Workshop “Translation Evaluation – From Fragmented Tools
and Data Sets to an Integrated Ecosystem”, Georg Rehm, Aljoscha Burchardt et al. (eds.). pp. 20-26. May 2016, Portorož, Slovenia.
This work has received funding from the European Union's Horizon 2020 research
and innovation programme under grant agreement no. 645452 (QT21). This work was
partially financially supported by the Government of Russian Federation, Grant
074-U01.
This work has been using language resources developed, stored and distributed
by the LINDAT/CLARIN project of the Ministry of Education, Youth and Sports of
the Czech Republic (project LM2010013).
Grammar Error Correction Corpus for Czech (GECCC) consists of 83 058 sentences and covers four diverse domains, including essays written by native students, informal website texts, essays written by Romani ethnic minority children and teenagers and essays written by nonnative speakers. All domains are professionally annotated for GEC errors in a unified manner, and errors were automatically categorized with a Czech-specific version of ERRANT released at https://github.com/ufal/errant_czech
The dataset was introduced in the paper Czech Grammar Error Correction with a Large and Diverse Corpus that was accepted to TACL. Until published in TACL, see the arXiv version: https://arxiv.org/pdf/2201.05590.pdf
Grammar Error Correction Corpus for Czech (GECCC) consists of 83 058 sentences and covers four diverse domains, including essays written by native students, informal website texts, essays written by Romani ethnic minority children and teenagers and essays written by nonnative speakers. All domains are professionally annotated for GEC errors in a unified manner, and errors were automatically categorized with a Czech-specific version of ERRANT released at https://github.com/ufal/errant_czech
The dataset was introduced in the paper Czech Grammar Error Correction with a Large and Diverse Corpus that was accepted to TACL. Until published in TACL, see the arXiv version: https://arxiv.org/pdf/2201.05590.pdf
This version fixes double annotation errors in train and dev M2 files, and also contains more metadata information.
Talks of Karel Makoň given to his friends in the course of late sixties through early nineties of the 20th century. The topic is mostly christian mysticism.
CzEng is a sentence-parallel Czech-English corpus compiled at the Institute of Formal and Applied Linguistics (ÚFAL). While the full CzEng 2.0 is freely available for non-commercial research purposes from the project website (https://ufal.mff.cuni.cz/czeng), this release contains only the original monolingual parts of news text (csmono 53M and enmono 79M sentences) with automatic (synthetic) translations by CUBBITT.
See the attached README for additional details such as the file format.
Pretrained model weights for the UDify model, and extracted BERT weights in pytorch-transformers format. Note that these weights slightly differ from those used in the paper.
This is the Czech data collected during the `VYSTADIAL` project. It is an extension of the 'Vystadial 2013' Czech part data release. The dataset comprises of telephone conversations in Czech, developed for training acoustic models for automatic speech recognition in spoken dialogue systems.