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A Parallel Corpus of Translationese

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Computational Linguistics and Intelligent Text Processing (CICLing 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9624))

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Abstract

We describe a set of bilingual English-French and English-German parallel corpora in which the direction of translation is accurately and reliably annotated. The corpora are diverse, consisting of parliamentary proceedings, literary works, transcriptions of TED talks and political commentary. They will be instrumental for research of translationese and its applications to (human and machine) translation; specifically, they can be used for the task of translationese identification, a research direction that enjoys a growing interest in recent years. To validate the quality and reliability of the corpora, we replicated previous results of supervised and unsupervised identification of translationese, and further extended the experiments to additional datasets and languages.

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Notes

  1. 1.

    All corpora are available at

    http://cl.haifa.ac.il/projects/translationese/index.shtml.

  2. 2.

    We use “EUR”, “HAN”, “LIT”, “TED” and “POL” to denote the five corpora hereafter.

  3. 3.

    The original Europarl is available from http://www.statmt.org/europarl/.

  4. 4.

    http://europa.eu/about-eu/facts-figures/administration/index_en.htm.

  5. 5.

    http://www.theguardian.com/education/datablog/2014/may/21/european-parliament-english-language-official-debates-data.

  6. 6.

    http://www.gutenberg.org.

  7. 7.

    http://farkastranslations.com/.

  8. 8.

    http://en.wikisource.org/.

  9. 9.

    TEDx are TED-like events not restricted to specific language. We could not find sufficient amount of TEDx German talks translated to English.

  10. 10.

    http://developer.ted.com/.

  11. 11.

    http://www.project-syndicate.org/.

  12. 12.

    http://www.diplomatisches-magazin.de/.

  13. 13.

    The list of French and German FW was downloaded from https://code.google.com/archive/p/stop-words/.

  14. 14.

    Feature combinations yield similar, occasionally slightly better, results; we refrain from providing full analysis in this paper.

  15. 15.

    Standard deviation in most experiments was close to 0.

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Acknowledgments

This research was supported by a grant from the Israeli Ministry of Science and Technology. We are grateful to Noam Ordan for much advice and encouragement. We also thank Sergiu Nisioi for helpful suggestions. We are grateful to Philipp Koehn for making the Europarl corpus available; to Cyril Goutte, George Foster and Pierre Isabelle for providing us with an annotated version of the Hansard corpus; to François Yvon and András Farkas (http://farkastranslations.com) for contributing their literary corpora; and to the TED OTP team for sharing TED talks and their translations. We thank also Raphael Salkie for sharing his diverse English-German corpus.

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Authors and Affiliations

  1. Department of Computer Science, University of Haifa, Haifa, Israel

    Ella Rabinovich & Shuly Wintner

  2. Department of Computational Linguistics, Universität des Saarlandes, Saarbrücken, Germany

    Ofek Luis Lewinsohn

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  1. Ella Rabinovich
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Correspondence to Ella Rabinovich .

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    Alexander Gelbukh

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Rabinovich, E., Wintner, S., Lewinsohn, O.L. (2018). A Parallel Corpus of Translationese. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2016. Lecture Notes in Computer Science(), vol 9624. Springer, Cham. https://doi.org/10.1007/978-3-319-75487-1_12

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