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Building a New-Generation Corpus for Empirical Translation Studies: The Dutch Parallel Corpus 2.0

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New Perspectives on Corpus Translation Studies

Part of the book series: New Frontiers in Translation Studies ((NFTS))


This chapter introduces a new, updated version of the Dutch Parallel Corpus, a bidirectional parallel corpus of expert translations for Dutch><English and Dutch><French language pairs. This revisited version of the corpus, which we dub Dutch Parallel Corpus 2.0, is dynamic in nature, and contains 2.75 million words at the time of writing. The corpus is sentence-aligned, lemmatized and POS-tagged using the state-of-the-art natural language processing toolkit Stanza. Compared to its predecessor, the Dutch Parallel Corpus 2.0 contains more metadata about the translators (e.g. gender, education, experience) and the translation projects (e.g. L1/L2 translation, software used, degree and type of revision), next to the traditional metadata about the texts themselves (e.g. source and target language, intended audience, intended goal, register). The availability of an extensive set of metadata is considered the main asset of this corpus, together with a more principled and flexible register classification, thus stimulating corpus-based translation scholars to answer more refined research questions about the linguistic and contextual factors that shape translated texts, and ultimately fostering ideas and theories about the social and cognitive processes involved in translation performance. The corpus is freely available for research purposes via

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  1. 1.

    The word count was calculated after the initial cleaning process of all texts (cf. Section 3). The eventual word count may deviate somewhat from this preliminary calculation.

  2. 2.

    Translation agencies which were not fully able to provide us with specific details on their employees were counted as a single translator, although more translators may have been involved in the translation process. This is clearly marked in the corpus. The numbers between parentheses refer to the amount of individual translators whose profile could be determined on the basis of all available metadata.

  3. 3.

    Such literary texts were retrieved from Project Gutenberg, an online library of free eBooks:

  4. 4.

    In contrast with texts of the original DPC-project, which are primarily outdated a decade after being produced, literary texts remain relevant to a higher extent. As such, they are better suited for reintegration in DPC 2.0.

  5. 5.

    Vanilla Aligner (Danielsson and Ridings 1997), Geometric Mapping and Alignment tool (Melamed 1997) and Microsoft Bilingual Aligner (Moore 2002).

  6. 6.

    AlignFactory Light was developed by the software company Terminotix:

  7. 7.

    With the exception of the last example (Ø: 1), which was taken from dpc2-img-000453-NL_EN, all other alignment types were extracted from dpc2-vbr-000244-NL_EN, which is a tourist brochure on the city of Bruges.

  8. 8.

  9. 9.

  10. 10.

  11. 11.

    For English, Dutch and French, the following language models were used, respectively: UD_English-EWT, UD_Dutch-Alpino and UD_French-GSD.

  12. 12.

    Initially, we did not include translators with a degree in interpreting or occasional translators, for instance.

  13. 13.

    Translator-specific criteria, such as age or gender, were often left unspecified in the questionnaire, since we regularly obtained a general overview of a translation department instead of a unique questionnaire for each translator.

  14. 14.

    As mentioned in Sect. 2, source texts had to be proper source texts, i.e. not translated from yet another source text. Nevertheless, DPC 2.0 contains four source texts which are translations themselves. In contrast with the original DPC, however, the inclusion of these texts was only accepted when the language of the original source text was known.

  15. 15.

    CAT-tools that were mentioned by the text providers are MemoQ, SDL Trados Studio, Déjà Vu X3 Professional, XTM and Wordfast. Post-edited texts were generated by either DeepL or Google Translate.

  16. 16.

    With domain expertise, we refer to translators’ subjective estimation of their expertise regarding a particular translation task and its topic(s).

  17. 17.

    These preliminary calculations were made on the basis of the main annotator’s initial labelling throughout the text-collection phase and do not account for doubtful cases, nor for hybrid contexts. The results of the interannotator agreement are expected to generate subtle modifications for the metadata channel, intended audience, communicative purpose and topic.

  18. 18.

    In addition to texts which were produced for an external audience, we were able to gather texts which are written for an internal target audience, in which organization-internal information is provided to a very specific, internal target audience. Texts which were produced for an internal audience are automatically classified as specialist.

  19. 19.

    The calculations in Table 5 were based on each text’s main communicative purpose. However, the register classification in DPC 2.0 (cf. Section 5) takes into consideration the presence of additional communicative goals within a single text.

  20. 20.

    Text provider and intended audience, respectively, refer to addressor and addressee mentioned in Biber & Conrad (2009).

  21. 21.

    As we mentioned in the previous section, these criteria were determined on the basis of the main annotator’s initial labelling, in anticipation of the in-depth analysis of the students’ ratings. All ratings will be added to the final corpus in order to allow for a more nuanced, fine-grained interpretation of (hybrid) situational criteria, depending on the specific aim(s) of each research project.

  22. 22.

    In order to retrieve literature or journalistic texts which discuss a touristic topic, we invite end-users of DPC 2.0 to further subdivide all registers according to this particular topic. Additionally, the flexibility of our approach equally allows for a topic-based classification of texts, regardless of their predefined situational characteristics.


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Questionnaire for translators

  1. 1.

    Documents or websites translated (please mention the title of each text):

  2. 2.

    Translation direction:

  3. 3.

    Collaborative translation:

  4. yes

  5. no

  6. 4.

    Translator’s gender:

  7. m

  8. f

  9. x

  10. 5.

    Translator’s degree:

  11. no specific language degree

  12. translation Master

  13. translation Bachelor

  14. language and literature

  15. interpreting

  16. 6.

    Experience as a translator (in years):

  17. 7.

    Translator’s year of birth:

  18. 8.

    Translation tools or memory involved:

  19. none, manual translation

  20. CAT-tool, i.e. ________________________________

  21. post-editing—machine translation, i.e. ____________________________

  22. 9.

    Translation directionality:

  23. (L1 (first language)

  24. (L2 (foreign language)

  25. 10.

    Translator’s status

  26. freelance

  27. in-house

  28. both

  29. 11.

    Use of style guides:

  30. in-house guidelines

  31. in-house glossary

  32. both

  33. none

  34. 12.

    Domain expertise (regarding the text’s topic)

  35. expert

  36. non-expert

  37. 13.

    External revision

  38. monolingual (only translation)

  39. bilingual (source text and translation)

  40. no revision

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Reynaert, R., Macken, L., Tezcan, A., De Sutter, G. (2021). Building a New-Generation Corpus for Empirical Translation Studies: The Dutch Parallel Corpus 2.0. In: Wang, V.X., Lim, L., Li, D. (eds) New Perspectives on Corpus Translation Studies. New Frontiers in Translation Studies. Springer, Singapore.

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