Data-driven information technology is revolutionising nearly all sectors of human life, including research, where advanced computational modelling of complex phenomena is gaining importance as a method of scientific inquiry across academia. The field of translation is no exception when it comes to the rise of data-driven technologies. However, in translation, the adoption of data-driven technologies is unevenly distributed across industry and academia. On the one hand, data-driven technologies based on artificial intelligence are boosting the language industry on an unprecedented scale. On the other hand, the academic discipline of translation studies continues using simplistic analytical tools despite the advances in data-driven research made in empirical translation studies and in related disciplines. Against this backdrop, the present thesis aims to strengthen data-driven research in translation studies along three interdependent lines of research: first, development of novel approaches to corpus building and the sustainable reuse of translation data; second, application of advanced computational techniques to corpus-based translation research; and, third, evaluation of novel forms of machine translation. In this way, the thesis is to promote methodological innovation in translation studies, and to narrow the infamous divide between translation studies and translation practice in an increasingly data-centric world.
Titelaufnahme
- TitelTranslation Studies, Corpora and Data-Driven Technology
- Weitere TitelTranslation Studies, Corpora and Data-Driven Technology
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- Erschienen
- AnmerkungAbweichender Titel laut Übersetzung der Verfasserin/des Verfassers
- Datum der AbgabeSeptember 2020
- SpracheEnglisch
- DokumenttypHabilitation
- Schlagwörter (DE)
- Schlagwörter (EN)
- URN
- Das Dokument ist online verfügbar
- Nachweis
Data-driven information technology is revolutionising nearly all sectors of human life, including research, where advanced computational modelling of complex phenomena is gaining importance as a method of scientific inquiry across academia. The field of translation is no exception when it comes to the rise of data-driven technologies. However, in translation, the adoption of data-driven technologies is unevenly distributed across industry and academia. On the one hand, data-driven technologies based on artificial intelligence are boosting the language industry on an unprecedented scale. On the other hand, the academic discipline of translation studies continues using simplistic analytical tools despite the advances in data-driven research made in empirical translation studies and in related disciplines. Against this backdrop, the present thesis aims to strengthen data-driven research in translation studies along three interdependent lines of research: first, development of novel approaches to corpus building and the sustainable reuse of translation data; second, application of advanced computational techniques to corpus-based translation research; and, third, evaluation of novel forms of machine translation. In this way, the thesis is to promote methodological innovation in translation studies, and to narrow the infamous divide between translation studies and translation practice in an increasingly data-centric world.
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