Google Translate versus DeepL in Spanish to English translation of Don Quixote

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 2015Corpas-Pastor, Gloria. 2015. “Translating English Verbal Collocations into Spanish: On Distribution and other Relevant Differences Related to Diatopic Variation.” Lingvisticæ Investigationes 381: 229–262. ; Shraiden and Mahadin 2015Shraiden, Khetam, and Radwam Salim Mahadin. 2015. “Difficulties and Strategies in Translating Collocations in BBC Political Texts.” Arab World English Journal (AWEJ) 6(3): 320–356. ). Literary translation continues to constitute one of the biggest challenges for machine translation (Toral and Way 2018Toral, Antonio, and Andy Way. 2018. “What Level of Quality Can Neural Machine Translation Attain on Literary Text?.” In Translation Quality Assessment. Machine Translation: Technologies and Applications, ed. by Joos Moorkens, Sheila Castilho, Federico Gaspari, and Stephen Doherty, 263–287. Dublin: Springer, Cham. ), where cohesion errors are amongst the most frequent (Voigt and Jurafsky 2012Voigt, Rob, and Dan Jurafsky. 2012. “Towards a Literary Machine Translation: The Role of Referencial Cohesion.” In Proceedings of the NAACL-HLT 2012 Workshop on Computational Linguistics for Literature, ed. by David Elson, Anna Kazantseva, Rada Mihalcea, and Stan Szpakowicz, 18–25. Montréal, Canada: Association for Computational Linguistics.). A comparative analysis of the translation of the first chapter of the world literature masterpiece El ingenioso hidalgo don Quijote de la Mancha — known as Don Quixote in English — was carried out, paying close attention to collocations. The human translation done by Tom Lathrop (Don Quixote) 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.