Abstract
Translation process research (TPR) has advanced in the recent years to a state which allows us to study “in great detail what source and target text units are being processed, at a given point in time, to investigate what steps are involved in this process, what segments are read and aligned and how this whole process is monitored” (Alves 2015, p. 32). We have sophisticated statistical methods and with the powerful tools to produce a better and more detailed understanding of the underlying cognitive processes that are involved in translation. Following Jakobsen (2011), who suspects that we may soon be in a situation which allows us to develop a computational model of human translation, Alves (2015) calls for a “clearer affiliation between TPR studies and a particular cognitive sciences paradigm” (p. 23).
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Notes
- 1.
In the SMT literature, these terms are often used slightly differently. Decoding is referred to as “the task of finding the best translation for a given input sentence, according to a given translation model .” (http://www.statmt.org/survey/Topic/Decoding), i.e. the combination of what we have split into decoding (of ST) and encoding (of the TT). This usage of the term is most likely due to Weaver’s original proposal in 1949 that “we can take the Russian original as an encrypted version of the English plaintext”, suggesting that the Russian author had actually an English text in mind but encoded it in Russian. Decoding the Russian source text would thus lead to the actual message in English. While the term “encoding” is not common at all and would, most likely, correspond to a training phase in which the underlying MT models are trained, here we adopt the terminology uses in the relevance theoretic literature.
- 2.
The degree of variation can be measured by means of perplexity: a flat distribution of different translation realizations leads to high entropy perplexity while a very pointed distribution (i.e. many translators produce the same translation) leads to low perplexity values.
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Carl, M., Schaeffer, M. (2019). Outline for a Relevance Theoretical Model of Machine Translation Post-editing. In: Li, D., Lei, V., He, Y. (eds) Researching Cognitive Processes of Translation. New Frontiers in Translation Studies. Springer, Singapore. https://doi.org/10.1007/978-981-13-1984-6_3
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