@inbook{Teich-etal2020-handbook,
title = {Translation, information theory and cognition},
author = {Elke Teich and Jos{\'e} Mart{\'i}nez Mart{\'i}nez and Alina Karakanta},
editor = {Fabio Alves and Arnt Lykke Jakobsen},
url = {https://www.taylorfrancis.com/chapters/edit/10.4324/9781315178127-24/translation-information-theory-cognition-elke-teich-josé-martínez-martínez-alina-karakanta},
year = {2020},
date = {2020},
booktitle = {The Routledge Handbook of Translation and Cognition},
isbn = {9781138037007},
pages = {360-375},
publisher = {Routledge},
address = {London, UK},
abstract = {<div>
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The chapter sketches a formal basis for the probabilistic modelling of human translation on the basis of information theory. We provide a definition of Shannon information applied to linguistic communication and discuss its relevance for modelling translation. We further explain the concept of the noisy channel and provide the link to modelling human translational choice. We suggest that a number of translation-relevant variables, notably (dis)similarity between languages, level of expertise and translation mode (i.e., interpreting vs. translation), may be appropriately indexed by entropy, which in turn has been shown to indicate production effort.

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pubstate = {published},
type = {inbook}
}