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Identifying Source-Language Dialects in Translation

Author

Listed:
  • Sergiu Nisioi

    (Human Language Technologies Center, Faculty of Mathematics and Computer Science, University of Bucharest, Academiei 14, 010014 Bucharest, Romania
    These authors contributed equally to this work.)

  • Ana Sabina Uban

    (Human Language Technologies Center, Faculty of Mathematics and Computer Science, University of Bucharest, Academiei 14, 010014 Bucharest, Romania
    These authors contributed equally to this work.)

  • Liviu P. Dinu

    (Human Language Technologies Center, Faculty of Mathematics and Computer Science, University of Bucharest, Academiei 14, 010014 Bucharest, Romania)

Abstract

In this paper, we aim to explore the degree to which translated texts preserve linguistic features of dialectal varieties. We release a dataset of augmented annotations to the Proceedings of the European Parliament that cover dialectal speaker information, and we analyze different classes of written English covering native varieties from the British Isles. Our analyses aim to discuss the discriminatory features between the different classes and to reveal words whose usage differs between varieties of the same language. We perform classification experiments and show that automatically distinguishing between the dialectal varieties is possible with high accuracy, even after translation, and propose a new explainability method based on embedding alignments in order to reveal specific differences between dialects at the level of the vocabulary.

Suggested Citation

  • Sergiu Nisioi & Ana Sabina Uban & Liviu P. Dinu, 2022. "Identifying Source-Language Dialects in Translation," Mathematics, MDPI, vol. 10(9), pages 1-14, April.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:9:p:1431-:d:800908
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    References listed on IDEAS

    as
    1. Moshe Koppel & Navot Akiva & Ido Dagan, 2006. "Feature instability as a criterion for selecting potential style markers," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(11), pages 1519-1525, September.
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    Cited by:

    1. Florentina Hristea & Cornelia Caragea, 2022. "Preface to the Special Issue “Natural Language Processing (NLP) and Machine Learning (ML)—Theory and Applications”," Mathematics, MDPI, vol. 10(14), pages 1-5, July.

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