Andy Way | School of Computing, Dublin City University
Contrary to perceived wisdom, we explore the role of machine translation (MT) in assisting with the translation of literary texts, considering both its limitations and its potential. Our motivations to explore this subject are twofold, arising from: (1) recent research advances in MT, and (2) the recent emergence of the ebook, which together allow us for the first time to build literature-specific MT systems by training statistical MT models on novels and their professional translations. A key challenge in literary translation is that one needs to preserve not only the meaning (as in other domains such as technical translation) but also the reading experience, so a literary translator needs to carefully select from the possible translation options. We explore the role of translation options in literary translation, especially in the context of the relatedness of the languages involved. We take Camus’ L’Étranger in the original French language and provide qualitative and quantitative analyses for its translations into English (a less-related language) and Italian (more closely related). Unsurprisingly, the MT output for Italian seems more straightforward to be post-edited. We also show that the performance of MT has improved over the last two years for this particular book, and that the applicability of MT does not only depend on the text to be translated but also on the type of translation that we are trying to produce. We then translate a novel from Spanish-to-Catalan with a literature-specific MT system. We assess the potential of this approach by discussing the translation quality of several representative passages.
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2002 “Discriminative Training and Maximum Entropy Models for Statistical Machine Translation. In ACL-2002: 40th Annual Meeting of the Association for Computational Linguistics, edited by Pierre Isabelle, 295–302. Stroudsburg, PA: ACL.
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2012Linguistic-based Patterns for Figurative Language Processing: The Case of Humor Recognition and Irony Detection. Doctoral Dissertation. Universitat Politècnica de València, Valencia, Spain.
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2015Computational Modelling of Metaphor. San Rafael, CA: Morgan and Claypool.
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2015 “Translating Literary Text between Related Languages Using SMT.
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DOLMACİ, Maide & Halil İbrahim BALKUL
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Flöter-Durr, Margarete
2022. Les limites épistémologiques des techniques numériques actuelles de l’intelligence artificielle en traduction. Lebende Sprachen 67:1 ► pp. 4 ff.
2019. CAN GOOGLE TRANSLATE BE TAUGHT TO TRANSLATE LITERATURE? A CASE FOR HUMANISTS TO COLLABORATE IN THE FUTURE OF MACHINE TRANSLATION. Translation Review 105:1 ► pp. 76 ff.
Kolb, Waltraud, Wolfgang U. Dressler & Elisa Mattiello
2022. How do machine translators measure up to human literary translators in stylometric tests?. Digital Scholarship in the Humanities 37:3 ► pp. 813 ff.
Li, Jun
2024. Optimization of Translation Techniques between English and Chinese Literary Works in the Information Age. Applied Mathematics and Nonlinear Sciences 9:1
Maggi, Ludovica & Sarah Bordes
2022. Intelligences en jeu. FORUM. Revue internationale d’interprétation et de traduction / International Journal of Interpretation and Translation 20:2 ► pp. 177 ff.
2022. Roy Youdale. Using Computers in the Translation of Literary Style: Challenges and Opportunities. Routledge, 2020. ISBN: 978-0-367-14123-3. Paperback £39.99. American, British and Canadian Studies 39:1 ► pp. 189 ff.
Rico, Celia
2022. Mind the gap. Babel. Revue internationale de la traduction / International Journal of Translation 68:5 ► pp. 697 ff.
Slessor, Stephen
2020. Tenacious technophobes or nascent technophiles? A survey of the technological practices and needs of literary translators. Perspectives 28:2 ► pp. 238 ff.
Taivalkoski-Shilov, Kristiina
2019. Ethical issues regarding machine(-assisted) translation of literary texts. Perspectives 27:5 ► pp. 689 ff.
Toral, Antonio & Andy Way
2018. What Level of Quality Can Neural Machine Translation Attain on Literary Text?. In Translation Quality Assessment [Machine Translation: Technologies and Applications, 1], ► pp. 263 ff.
Tîrban, Emilian
2023. On the Efficiency and Efficacy of Machine-Assisted Literary Translation: A Case Study for English/Romanian and Romanian/English Machine-Assisted Translation. East-West Cultural Passage 23:2 ► pp. 59 ff.
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