Dutch Parallel Corpus: A Balanced Copyright-Cleared Parallel Corpus

Macken, Lieve; Clercq, Orphée De; Paulussen, Hans

doi:10.7202/1006182ar

Cited by 70 publications

(26 citation statements)

References 21 publications

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“…Chesterman 2004;Klaudy 2001;Olohan & Baker 2000;Øverås 1998). On the other hand, however, the article and the research that resulted from it were criticized as well (see e.g.…”

Section: Theoretical Background 21 Traditions In Translation Studiesmentioning

confidence: 97%

Applying a multidimensional, register-sensitive approach to visualize normalization in translated and non-translated Dutch

Delaere¹,

Sutter²

2013

BJL

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In this paper we investigate the differences in risk-averse behavior in translated versus non-translated texts by comparing lexical normalization in various registers of translated and non-translated Dutch. We want to verify: (i) to what extent normalization is register dependent; (ii) whether normalizing behavior is similar in translated and non-translated texts of the same register, and (iii) to what extent normalization is source-language dependent. We relied on the Dutch Parallel Corpus to investigate the dispersion of 10 profiles, i.e. sets of synonymous lexical alternatives consisting of a Standard Dutch and a Belgian Standard Dutch alternative. Using an exploratory, multivariate technique we visualized and measured the degrees to which a number of registers of translated and non-translated Dutch conform to linguistic norms.

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“…Chesterman 2004;Klaudy 2001;Olohan & Baker 2000;Øverås 1998). On the other hand, however, the article and the research that resulted from it were criticized as well (see e.g.…”

Section: Theoretical Background 21 Traditions In Translation Studiesmentioning

confidence: 97%

Applying a multidimensional, register-sensitive approach to visualize normalization in translated and non-translated Dutch

Delaere¹,

Sutter²

2013

BJL

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“…These manually created reference alignments can be used to develop or test automatic word alignment systems. For more information on the sub-sentential alignments, we refer to [17].…”

Section: Fig 111 Alignment Spot Checkmentioning

confidence: 99%

“…Table 11.1). For a detailed description of the DPC corpus design and text typology, we refer to [17,24].…”

Section: Balanced Corpus Designmentioning

confidence: 99%

Dutch Parallel Corpus: A Balanced Parallel Corpus for Dutch-English and Dutch-French

Paulussen

Macken

Vandeweghe

et al. 2012

Essential Speech and Language Technology for Dutch

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Parallel corpora are a valuable resource for researchers across a wide range of disciplines, i.e. machine translation, computer-assisted translation, terminology extraction, computer-assisted language learning, contrastive linguistics and translation studies. Since the development of a high-quality parallel corpus is a time-consuming and costly process, the DPC project aimed at the creation of a multifunctional resource that satisfies the needs of this diverse group of disciplines.The resulting corpus-the Dutch Parallel Corpus (DPC)-is a ten-millionword, sentence-aligned, linguistically enriched parallel corpus for the language pairs Dutch-English and Dutch-French. As the DPC is bidirectional, the corpus can also be used as comparable corpus to study the differences between translated versus non-translated language. A small part of the corpus is trilingual. The DPC distinguishes itself from other parallel corpora by having a balanced composition (both in terms of text types and translation directions), by its availability to the wide research community thanks to its copyright clearance and by focusing on quality rather than quantity.

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“…The source sentences in this data set were extracted from three different text types of the Dutch Parallel Corpus (Macken et al, 2011). The translations in this data set were obtained from Google Translate 4 .…”

Section: Detecting Grammatical Errorsmentioning

confidence: 99%

A Neural Network Architecture for Detecting Grammatical Errors in Statistical Machine Translation

Tezcan¹,

Hoste²,

Macken³

2017

The Prague Bulletin of Mathematical Linguistics

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In this paper we present a Neural Network (NN) architecture for detecting grammatical errors in Statistical Machine Translation (SMT) using monolingual morpho-syntactic word representations in combination with surface and syntactic context windows. We test our approach on two language pairs and two tasks, namely detecting grammatical errors and predicting overall post-editing effort. Our results show that this approach is not only able to accurately detect grammatical errors but it also performs well as a quality estimation system for predicting overall post-editing effort, which is characterised by all types of MT errors. Furthermore, we show that this approach is portable to other languages.

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Dutch Parallel Corpus: A Balanced Copyright-Cleared Parallel Corpus

Cited by 70 publications

References 21 publications

Applying a multidimensional, register-sensitive approach to visualize normalization in translated and non-translated Dutch

Applying a multidimensional, register-sensitive approach to visualize normalization in translated and non-translated Dutch

Dutch Parallel Corpus: A Balanced Parallel Corpus for Dutch-English and Dutch-French

A Neural Network Architecture for Detecting Grammatical Errors in Statistical Machine Translation

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