Could neural machine translation be used as a self-care tool to support literary translators to process emotions when translating challenging texts on grief?

Diplôme Master Could neural machine translation be used as a self-care tool to support literary translators to process emotions when translating challenging texts on grief? eng 2025 author Fox Catherine Theresa 987896 director Pickford Susan 973177 Services communs du département de traduction (TRACO) <p>This thesis looks at the topical issues of translator wellbeing and self-care, and the growing role of machine translation (MT) and Artificial Intelligence (AI) in the translation sector. The aim of this study is to determine whether neural machine translation could be used as a self-care tool for literary translators working on emotionally challenging texts. This issue is examined through an experiment carried out with volunteer student translators, who were asked to assess their experience of translating two comparable sections of an emotionally challenging text on grief with and without MT. A case study of a professional translator’s experience of grief and its impact on her translation work is also presented. The results suggest that the use of MT to translate emotionally taxing texts may have something of a distancing effect, reducing the emotional impact on the translator by requiring less emotional and cognitive effort.</p> Artificial Intelligence (AI) Emotion Grief Machine translation (MT) Translator self-care Trauma Wellbeing Dewey 418.02 Maîtrise universitaire en traduction spécialisée 111 Département de traduction 91 false bc7004ed-dc50-4537-bdfd-2070d6dc0ba7 Master thesis Public Memoire_FOX_Catherine_Final_Jan25.pdf 1183605 application/pdf 863593b04d66ebe90817a0ad3802d9986e8c2477370684165e277c9deff30170 openAccess Faculté de traduction et d'interprétation / Département de traduction

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Could neural machine translation be used as a self-care tool to support literary translators to process emotions when translating challenging texts on grief?				2025	116	3,423

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