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Volume 11, Issue 1
  • ISSN 2352-1805
  • E-ISSN: 2352-1813
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Abstract

Abstract

This paper analyses the effectiveness of neural machine translation when applied to literary translation and, more specifically, to the translation of collocations, one of the most difficult aspects in machine translation (Corpas-Pastor 2015; Shraiden and Mahadin 2015). Literary translation continues to constitute one of the biggest challenges for machine translation (Toral and Way 2018), where cohesion errors are amongst the most frequent (Voigt and Jurafsky 2012). A comparative analysis of the translation of the first chapter of the world literature masterpiece — known as in English — was carried out, paying close attention to collocations. The human translation done by Tom Lathrop () was compared to the target texts obtained with the two biggest neural machine translation systems today, Google Translate and DeepL, to see which provided more accurate results. The results confirm that neural machine translation offers highly reliable results. On a quantitative level the margins are very narrow when determining which system, DeepL or Google Translate, is better. DeepL scored better in terms of accuracy and recall, but in the BLEU metrics Google Translate scored 28.10 and DeepL 26.63. On a qualitative level and from a subjective point of view, we found DeepL’s translation to be somewhat more fluid and natural than Google Translate’s.

© 2025 John Benjamins Publishing Company
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/content/journals/10.1075/ttmc.00154.iba
2025-01-07
2025-01-20

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/content/journals/10.1075/ttmc.00154.iba
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Google Translate versus DeepL in Spanish to English translation of Don Quixote
Translation and Translanguaging in Multilingual Contexts 11, 65 (2025); https://doi.org/10.1075/ttmc.00154.iba
/content/journals/10.1075/ttmc.00154.iba
/content/journals/10.1075/ttmc.00154.iba
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  • Article Type: Research Article
Keyword(s): collocations; literary translation; neural machine translation

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