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1. WMT16 Tuning Shared Task Models (Czech-to-English)

Creator:
Kamran, Amir, Jawaid, Bushra, Bojar, Ondřej, and Stanojevic, Milos
Publisher:
Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL) and University of Amsterdam, ILLC
Type:
text and corpus
Subject:
WMT16, machine translation, tuning, baseline models, and shared task
Language:
Czech and English
Description:
The item contains models to tune for the WMT16 Tuning shared task for Czech-to-English. CzEng 1.6pre (http://ufal.mff.cuni.cz/czeng/czeng16pre) corpus is used for the training of the translation models. The data is tokenized (using Moses tokenizer), lowercased and sentences longer than 60 words and shorter than 4 words are removed before training. Alignment is done using fast_align (https://github.com/clab/fast_align) and the standard Moses pipeline is used for training. Two 5-gram language models are trained using KenLM: one only using the CzEng English data and the other is trained using all available English mono data for WMT except Common Crawl. Also included are two lexicalized bidirectional reordering models, word based and hierarchical, with msd conditioned on both source and target of processed CzEng.
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

2. WMT16 Tuning Shared Task Models (English-to-Czech)

Creator:
Kamran, Amir, Jawaid, Bushra, Bojar, Ondřej, and Stanojevic, Milos
Publisher:
Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL) and University of Amsterdam, ILLC
Type:
text and corpus
Subject:
WMT16, machine translation, tuning, baseline models, and shared task
Language:
English and Czech
Description:
This item contains models to tune for the WMT16 Tuning shared task for English-to-Czech. CzEng 1.6pre (http://ufal.mff.cuni.cz/czeng/czeng16pre) corpus is used for the training of the translation models. The data is tokenized (using Moses tokenizer), lowercased and sentences longer than 60 words and shorter than 4 words are removed before training. Alignment is done using fast_align (https://github.com/clab/fast_align) and the standard Moses pipeline is used for training. Two 5-gram language models are trained using KenLM: one only using the CzEng Czech data and the other is trained using all available Czech mono data for WMT except Common Crawl. Also included are two lexicalized bidirectional reordering models, word based and hierarchical, with msd conditioned on both source and target of processed CzEng.
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