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100992. Wjtek, wčelař a spolu aulař, nebo, Potěssitedlná naučenj pro wssechny ty, kteřj wčely chowagj nebo chowati mohau
- Creator:
- Oettl, Jan Nep. and Dundr, Josef Alexandr
- Publisher:
- C. k. wlast. hosp. společnost w králowstwj českém
- Format:
- regular print and viii, 148 s., [2] slož. obr. příl. (22 x 29 cm) : il. ; 22 cm
- Type:
- handbook, příručky, staré tisky, model:monograph, text, and TEXT
- Subject:
- udc:638, Konspekt:24, udc:094, Konspekt:12, udc:638.1, udc:(035), Konspekt:Včelařství, Konspekt:Staré tisky, agroterm:včelařství, agroterm:včely, agroterm:chov včel, and agroterm:úly
- Language:
- Czech and German
- Description:
- (Statement of Responsibility) od gich kragana Jana Nep. Oettla ; přeložil Josef Alex. Dunder, Přeloženo z němčiny, and (Language) Tištěno švabachem
- Rights:
- http://creativecommons.org/publicdomain/mark/1.0/ and policy:public
100993. WMT 13 Test Set
- Creator:
- Hoang, Duc Tam and Bojar, Ondřej
- Publisher:
- Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
- Type:
- text and corpus
- Subject:
- test data, parallel corpus, and Vietnamese
- Language:
- Vietnamese, Czech, English, German, French, Spanish, and Russian
- Description:
- We provide the Vietnamese version of the multi-lingual test set from WMT 2013 [1] competition. The Vietnamese version was manually translated from English. For completeness, this record contains the 3000 sentences in all the WMT 2013 original languages (Czech, English, French, German, Russian and Spanish), extended with our Vietnamese version. Test set is used in [2] to evaluate translation between Czech, English and Vietnamese. References 1. http://www.statmt.org/wmt13/evaluation-task.html 2. Duc Tam Hoang and Ondřej Bojar, The Prague Bulletin of Mathematical Linguistics. Volume 104, Issue 1, Pages 75--86, ISSN 1804-0462. 9/2015
- Rights:
- Creative Commons - Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0), http://creativecommons.org/licenses/by-nc-sa/4.0/, and PUB
100994. WMT16 APE Shared Task Data
- Creator:
- Turchi, Marco, Chatterjee, Rajen, and Negri, Matteo
- Publisher:
- Fondazione Bruno Kessler, Trento, Italy
- Type:
- text and corpus
- Subject:
- machine translation, machine learning, automatic postediting, and shared task
- Language:
- English and German
- Description:
- Training, development and text data (the same used for the Sentence-level Quality Estimation task) consist in English-German triplets (source, target and post-edit) belonging to the IT domain and already tokenized. Training and development respectively contain 12,000 and 1,000 triplets, while the test set 2,000 instances. All data is provided by the EU project QT21 (http://www.qt21.eu/).
- Rights:
- AGREEMENT ON THE USE OF DATA IN QT21 APE Task, https://lindat.mff.cuni.cz/repository/xmlui/page/licence-TAUS_QT21, and PUB
100995. WMT16 APE Shared Task Data - Reference sentences
- Creator:
- Turchi, Marco, Negri, Matteo, and Chatterjee, Rajen
- Publisher:
- Fondazione Bruno Kessler, Trento, Italy
- Type:
- text and corpus
- Subject:
- machine translation, machine learning, automatic post-editing, and shared task
- Language:
- German
- Description:
- Training, development and test data consist in German sentences belonging to the IT domain and already tokenized. These sentences are the references of the data released for the 2016 edition of the WMT APE shared task. Differently from the data previously released, these sentences are obtained by manually translating the source sentence without leveraging the raw mt outputs. Training and development respectively contain 12,000 and 1,000 segments, while the test set 2,000 items. All data is provided by the EU project QT21 (http://www.qt21.eu/).
- Rights:
- AGREEMENT ON THE USE OF DATA IN QT21 APE Task, https://lindat.mff.cuni.cz/repository/xmlui/page/licence-TAUS_QT21, and PUB
100996. WMT16 Quality Estimation Shared Task Training and Development Data
- Creator:
- Specia, Lucia, Logacheva, Varvara, and Scarton, Carolina
- Publisher:
- University of Sheffield
- Type:
- text and corpus
- Subject:
- machine translation, quality estimation, and machine learning
- Language:
- English and German
- Description:
- Training and development data for the WMT16 QE task. Test data will be published as a separate item. This shared task will build on its previous four editions to further examine automatic methods for estimating the quality of machine translation output at run-time, without relying on reference translations. We include word-level, sentence-level and document-level estimation. The sentence and word-level tasks will explore a large dataset produced from post-editions by professional translators (as opposed to crowdsourced translations as in the previous year). For the first time, the data will be domain-specific (IT domain). The document-level task will use, for the first time, entire documents, which have been human annotated for quality indirectly in two ways: through reading comprehension tests and through a two-stage post-editing exercise. Our tasks have the following goals: - To advance work on sentence and word-level quality estimation by providing domain-specific, larger and professionally annotated datasets. - To study the utility of detailed information logged during post-editing (time, keystrokes, actual edits) for different levels of prediction. - To analyse the effectiveness of different types of quality labels provided by humans for longer texts in document-level prediction. This year's shared task provides new training and test datasets for all tasks, and allows participants to explore any additional data and resources deemed relevant. A in-house MT system was used to produce translations for the sentence and word-level tasks, and multiple MT systems were used to produce translations for the document-level task. Therefore, MT system-dependent information will be made available where possible.
- Rights:
- AGREEMENT ON THE USE OF DATA IN QT21, https://lindat.mff.cuni.cz/repository/xmlui/page/licence-TAUS_QT21, and PUB
100997. WMT17 De-En APE Shared Task Data
- Creator:
- Turchi, Marco, Chatterjee, Rajen, and Negri, Matteo
- Publisher:
- Fondazione Bruno Kessler, Trento, Italy
- Type:
- text and corpus
- Subject:
- machine translation, shared task, automatic post-editing, and post-editing
- Language:
- German and English
- Description:
- Training and development data for the WMT 2017 Automatic post-editing task (the same used for the Sentence-level Quality Estimation task). They consist in German-English triplets (source, target and post-edit) belonging to the pharmacological domain and already tokenized. Training and development respectively contain 25,000 and 1,000 triplets. All data is provided by the EU project QT21 (http://www.qt21.eu/).
- Rights:
- AGREEMENT ON THE USE OF DATA IN QT21 APE Task, https://lindat.mff.cuni.cz/repository/xmlui/page/licence-TAUS_QT21, and PUB
100998. WMT17 En-De APE Shared Task Data
- Creator:
- Turchi, Marco, Chatterjee, Rajen, and Negri, Matteo
- Publisher:
- Fondazione Bruno Kessler, Trento, Italy
- Type:
- text and corpus
- Subject:
- machine translation, shared task, post-editing, and automatic post-editing
- Language:
- English and German
- Description:
- Training data for the WMT 2017 Automatic post-editing task (the same used for the Sentence-level Quality Estimation task). They consist in 11,000 English-German triplets (source, target and post-edit) belonging to the IT domain and already tokenized. All data is provided by the EU project QT21 (http://www.qt21.eu/).
- Rights:
- AGREEMENT ON THE USE OF DATA IN QT21 APE Task, https://lindat.mff.cuni.cz/repository/xmlui/page/licence-TAUS_QT21, and PUB
100999. WMT17 Quality Estimation Shared Task Training and Development Data
- Creator:
- Specia, Lucia and Logacheva, Varvara
- Publisher:
- University of Sheffield
- Type:
- text and corpus
- Subject:
- machine translation, quality estimation, and machine learning
- Language:
- English and German
- Description:
- Training and development data for the WMT17 QE task. Test data will be published as a separate item. This shared task will build on its previous five editions to further examine automatic methods for estimating the quality of machine translation output at run-time, without relying on reference translations. We include word-level, phrase-level and sentence-level estimation. All tasks will make use of a large dataset produced from post-editions by professional translators. The data will be domain-specific (IT and Pharmaceutical domains) and substantially larger than in previous years. In addition to advancing the state of the art at all prediction levels, our goals include: - To test the effectiveness of larger (domain-specific and professionally annotated) datasets. We will do so by increasing the size of one of last year's training sets. - To study the effect of language direction and domain. We will do so by providing two datasets created in similar ways, but for different domains and language directions. - To investigate the utility of detailed information logged during post-editing. We will do so by providing post-editing time, keystrokes, and actual edits. This year's shared task provides new training and test datasets for all tasks, and allows participants to explore any additional data and resources deemed relevant. A in-house MT system was used to produce translations for all tasks. MT system-dependent information can be made available under request. The data is publicly available but since it has been provided by our industry partners it is subject to specific terms and conditions. However, these have no practical implications on the use of this data for research purposes.
- Rights:
- AGREEMENT ON THE USE OF DATA IN QT21, https://lindat.mff.cuni.cz/repository/xmlui/page/licence-TAUS_QT21, and PUB
101000. WMT17 Quality Estimation Shared Test Data
- Creator:
- Specia, Lucia and Logacheva, Varvara
- Publisher:
- University of Sheffield
- Type:
- text and corpus
- Subject:
- machine translation, quality estimation, and machine learning
- Language:
- English and German
- Description:
- Test data for the WMT17 QE task. Train data can be downloaded from http://hdl.handle.net/11372/LRT-1974 This shared task will build on its previous five editions to further examine automatic methods for estimating the quality of machine translation output at run-time, without relying on reference translations. We include word-level, phrase-level and sentence-level estimation. All tasks will make use of a large dataset produced from post-editions by professional translators. The data will be domain-specific (IT and Pharmaceutical domains) and substantially larger than in previous years. In addition to advancing the state of the art at all prediction levels, our goals include: - To test the effectiveness of larger (domain-specific and professionally annotated) datasets. We will do so by increasing the size of one of last year's training sets. - To study the effect of language direction and domain. We will do so by providing two datasets created in similar ways, but for different domains and language directions. - To investigate the utility of detailed information logged during post-editing. We will do so by providing post-editing time, keystrokes, and actual edits. This year's shared task provides new training and test datasets for all tasks, and allows participants to explore any additional data and resources deemed relevant. A in-house MT system was used to produce translations for all tasks. MT system-dependent information can be made available under request. The data is publicly available but since it has been provided by our industry partners it is subject to specific terms and conditions. However, these have no practical implications on the use of this data for research purposes.
- Rights:
- AGREEMENT ON THE USE OF DATA IN QT21, https://lindat.mff.cuni.cz/repository/xmlui/page/licence-TAUS_QT21, and PUB