Announcing CzEng 2.0 Parallel Corpus with over 2 Gigawords
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Abstract (arXiv)
We present a new release of the Czech-English parallel corpus CzEng 2.0 consisting of over 2 billion words (2 "gigawords") in each language. The corpus contains document-level information and is filtered with several techniques to lower the amount of noise. In addition to the data in the previous version of CzEng, it contains new authentic and also high-quality synthetic parallel data. CzEng is freely available for research and educational purposes.
Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Jul 2020.
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Citing papers per year: 2021: 8; 2022: 3; 2023: 4; 2024: 2.
17 papers.
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- 2021Sequence Length is a Domain: Length-based Overfitting in Transformer Modelscited by 34
- 2021Why don't people use character-level machine translation?cited by 27
- 2021Improving Paraphrase Detection with the Adversarial Paraphrasing Taskcited by 19
- 2022Czech Grammar Error Correction with a Large and Diverse Corpuscited by 17
- 2023Unveiling Multilinguality in Transformer Models: Exploring Language Specificity in Feed-Forward Networkscited by 12
- 2021Backtranslation Feedback Improves User Confidence in MT, Not Qualitycited by 10
- 2021Understanding Model Robustness to User-generated Noisy Textscited by 8
- 2021End-to-End Lexically Constrained Machine Translation for Morphologically Rich Languagescited by 6
- 2023Breeding Machine Translations: Evolutionary approach to survive and thrive in the world of automated evaluationcited by 5
- 2023Some Like It Small: Czech Semantic Embedding Models for Industry Applicationscited by 3
- 2021Sampling and Filtering of Neural Machine Translation Distillation Datacited by 2
- 2022CUNI Submission in WMT22 General Taskcited by 2
5 more
- 2022CUNI Systems for the WMT22 Czech-Ukrainian Translation Taskcited by 2
- 2024Charles Translator: A Machine Translation System between Ukrainian and Czechcited by 2
- 2023Negative Lexical Constraints in Neural Machine Translationcited by 1
- 2021Leveraging Neural Machine Translation for Word Alignmentcited by 0
- 2024A Comparative Study of Text Retrieval Models on DaReCzechcited by 0
- 2024A Comparative Study of Text Retrieval Models on DaReCzech19 Nov 2024
- 2024Charles Translator: A Machine Translation System between Ukrainian and Czech10 Apr 2024
- 2023Some Like It Small: Czech Semantic Embedding Models for Industry Applications23 Nov 2023
- 2023Unveiling Multilinguality in Transformer Models: Exploring Language Specificity in Feed-Forward Networks24 Oct 2023
- 2023Negative Lexical Constraints in Neural Machine Translation7 Aug 2023
- 2023Breeding Machine Translations: Evolutionary approach to survive and thrive in the world of automated evaluation30 May 2023
- 2022CUNI Systems for the WMT22 Czech-Ukrainian Translation Task1 Dec 2022
- 2022CUNI Submission in WMT22 General Task29 Nov 2022
- 2022Czech Grammar Error Correction with a Large and Diverse Corpus14 Jan 2022
- 2021Why don't people use character-level machine translation?15 Oct 2021
- 2021Understanding Model Robustness to User-generated Noisy Texts14 Oct 2021
- 2021Sequence Length is a Domain: Length-based Overfitting in Transformer Models15 Sep 2021
5 more
- 2021End-to-End Lexically Constrained Machine Translation for Morphologically Rich Languages23 Jun 2021
- 2021Improving Paraphrase Detection with the Adversarial Paraphrasing Task14 Jun 2021
- 2021Backtranslation Feedback Improves User Confidence in MT, Not Quality12 Apr 2021
- 2021Sampling and Filtering of Neural Machine Translation Distillation Data1 Apr 2021
- 2021Leveraging Neural Machine Translation for Word Alignment31 Mar 2021
- 2021Sequence Length is a Domain: Length-based Overfitting in Transformer Modelscited by 34
- 2021Why don't people use character-level machine translation?cited by 27
- 2021Improving Paraphrase Detection with the Adversarial Paraphrasing Taskcited by 19
- 2022Czech Grammar Error Correction with a Large and Diverse Corpuscited by 17
- 2023Unveiling Multilinguality in Transformer Models: Exploring Language Specificity in Feed-Forward Networkscited by 12
- 2021Backtranslation Feedback Improves User Confidence in MT, Not Qualitycited by 10
- 2021Understanding Model Robustness to User-generated Noisy Textscited by 8
- 2021End-to-End Lexically Constrained Machine Translation for Morphologically Rich Languagescited by 6
- 2023Breeding Machine Translations: Evolutionary approach to survive and thrive in the world of automated evaluationcited by 5
- 2023Some Like It Small: Czech Semantic Embedding Models for Industry Applicationscited by 3
- 2021Sampling and Filtering of Neural Machine Translation Distillation Datacited by 2
- 2022CUNI Submission in WMT22 General Taskcited by 2
12 most cited of 17 All 17 (JSON)
- 2024A Comparative Study of Text Retrieval Models on DaReCzech19 Nov 2024
- 2024Charles Translator: A Machine Translation System between Ukrainian and Czech10 Apr 2024
- 2023Some Like It Small: Czech Semantic Embedding Models for Industry Applications23 Nov 2023
- 2023Unveiling Multilinguality in Transformer Models: Exploring Language Specificity in Feed-Forward Networks24 Oct 2023
- 2023Negative Lexical Constraints in Neural Machine Translation7 Aug 2023
- 2023Breeding Machine Translations: Evolutionary approach to survive and thrive in the world of automated evaluation30 May 2023
- 2022CUNI Systems for the WMT22 Czech-Ukrainian Translation Task1 Dec 2022
- 2022CUNI Submission in WMT22 General Task29 Nov 2022
- 2022Czech Grammar Error Correction with a Large and Diverse Corpus14 Jan 2022
- 2021Why don't people use character-level machine translation?15 Oct 2021
- 2021Understanding Model Robustness to User-generated Noisy Texts14 Oct 2021
- 2021Sequence Length is a Domain: Length-based Overfitting in Transformer Models15 Sep 2021
12 newest of 17 All 17 (JSON)