CzEng 1.6: Enlarged Czech-English Parallel Corpus with Processing Tools Dockered

By Ondrej Bojar, Ondrej Dusek, Tom Kocmi, Jindřich Libovický, Michal Novák, Martin Popel, Roman Sudarikov, Dusan Varis. 2016

97 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Natural Language Processing, Evaluation & Benchmarks

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Citations in the corpus

Findings of the 2016 Conference on Machine Translation (2016, background): “…post-editing (§7) The conference featured additional shared tasks that are described in separate papers in these proceedings: • tuning (Jawaid et al., 2016) • metrics (Bojar et al., 2016b) • cross-lingual pronoun prediction (Guillou et al., 2016) • multimodal machine translation and…”
Edinburgh Neural Machine Translation Systems for WMT 16 (2016, positive): “For English → Czech, we trained our baseline model on the complete WMT16 parallel training set (including CzEng 1.6pre (Bojar et al., 2016)), until we observed convergence on our heldout set (newstest2014).”
Adversarial Example Generation with Syntactically Controlled Paraphrase Networks (2018, methodology): “This corpus consists of over 50 million paraphrases obtained by backtranslating the Czech side of the CzEng (Bojar et al., 2016) parallel corpus.”
Findings of the 2018 Conference on Machine Translation (WMT18) (2018, neutral): “As established in 2016 (Bojar et al., 2016a), DA results (via crowd-sourcing) and RR results (produced by researchers) correlate strongly, with Pearson correlation ranging from 0.920 to 0.997 across several source languages into English and at 0.975 for English-to-Russian (the only pair evaluated…”
Findings of the 2017 Conference on Machine Translation (WMT17) (2017, methodology): “In (Bojar et al., 2016a) we considered the large differences in repetitiveness between APE15 and APE16 data as a possible motivation for the significant baseline improvements achieved by participants in the second round of the task.”
ParaCrawl: Web-Scale Acquisition of Parallel Corpora (2020, background): “A large Czech–English corpus from various sources was collected (Bojar et al., 2010), linguistically annotated (Bojar et al., 2012), and has been continuously extended to over 300 million words (Bojar et al., 2016).”
ParaNMT-50M: Pushing the Limits of Paraphrastic Sentence Embeddings with Millions of Machine Translations (2017, background): “6 (Bojar et al., 2016), Europarl, and News Commentary. We did not choose Czech due to any particular linguistic properties. Wieting et al. (2017) found little difference among Czech,”
The University of Edinburgh’s Neural MT Systems for WMT17 (2017, methodology): “To create the parallel corpus, we take the whole of CzEng 1.6pre (Bojar et al., 2016), plus the latest WMT releases of Europarl, News-commentary and CommonCrawl.”
FINDINGS OF THE IWSLT 2020 EVALUATION CAMPAIGN (2020, background): “…Speech-Translation TED corpus.23 For English→Czech Translation: • MuST-C (Di Gangi et al., 2019a), release 1.1 contains English-Czech pair, • CzEng 1.7 (Bojar et al., 2016).24 Note that CzEng overlaps with English-German test data of the Offline Speech Translation Task so it was not allowed…”
Reformulating Unsupervised Style Transfer as Paraphrase Generation (2020, methodology): “A.1 PARA NMT-50M Filtering Details We train our paraphrase model in a seq2seq fashion using the PARA NMT-50M corpus (Wieting and Gimpel, 2018), which was constructed by back-translating (Sennrich et al., 2016) the Czech side of the CzEng parallel corpus (Bojar et al., 2016).”
CUNI Submissions in WMT18 (2018, methodology): “These systems are Chimera2016 (Tamchyna et al., 2016; Bojar et al., 2016b), NeuralMonkey (Helcl et al., 2018) 2 and Marian (where the translation part was formerly known as AmuNMT) (Junczys-Dowmunt et al., 2016) with pretrained English-to-Czech Nematus models.”
Pushing the Limits of Paraphrastic Sentence Embeddings with Millions of Machine Translations (2017, background): “6 (Bojar et al., 2016) has many of these properties in comparison to other available corpora, we calculate some statistics between other possible data sources for creating a paraphrase corpus.”
A Multilingual View of Unsupervised Machine Translation (2020, methodology): “For parallel data, we used the UN Corpus (Ziemski et al., 2016) for English-Spanish, the 109 FrenchEnglish Gigaword corpus3 for the English-French and the CzEng 1.7 dataset (Bojar et al., 2016) for English-Czech.”
Simple and Effective Paraphrastic Similarity from Parallel Translations (2019, unknown): “6 English-Czech parallel corpus (Bojar et al., 2016).”