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Predictive Turn in Translation Studies: Review and Prospects

  • Moritz SchaefferEmail author
  • Jean Nitzke
  • Silvia Hansen-Schirra
Reference work entry

Abstract

Translation studies – like other disciplines – are influenced by digitization, big data analytics, and artificial intelligence. Two major scientific developments exploit digital and data-driven methods and are currently triggering a “predictive turn” in translation studies: machine learning approaches to translation and computational modelling of the human translation process. This chapter presents a literature review of these two areas and explains the development of the “predictive turn.” The impact of machine translation on the market, practice, and theory of translation changes the way a translation is defined; it is not necessarily only a human product or service anymore. Using behavioral and imaging techniques – e.g., eye tracking, electroencephalography (EEG), and functional magnetic resonance imaging (fMRI) – the hitherto speculatively investigated translation process is becoming increasingly predictable. Machine translation aims to predict the product of the translation process which used to be human only and which is being modelled by translation process researchers. The ever-tighter integration of human and machine means that risks resulting from this interaction (e.g., in terms of error rates) must be calculated differently. While not yet possible, a fully implemented model of the translation process which can predict when and why a translator is having trouble carrying out the task could be integrated with a machine translation system and which behaves in such a way as to provide the necessary information the human needs to solve the problem at hand. It is also very likely that machine translation systems will learn from the human process so that they will eventually become even better at predicting what and how the human would translate. The integration between human and machine will, just like in all other aspects of life, become more intimate, radicalizing the risks and benefits associated with this conversation between mathematical models and human behavior and cognition.

Keywords

Translation process Translation behavior Machine translation Post-editing 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Moritz Schaeffer
    • 1
    Email author
  • Jean Nitzke
    • 1
  • Silvia Hansen-Schirra
    • 1
  1. 1.Johannes Gutenberg University MainzGermersheimGermany

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