Identifying indices of effort in post-editing of machine translation can have a number of applications, including estimating machine translation quality and calculating post-editors' pay rates. Both source-text and machine-output features as well as subjects' traits are investigated here in view of their impact on cognitive effort, which is measured with eye tracking and a subjective scale borrowed from the field of Educational Psychology. Data is analysed with mixed-effects models, and results indicate that the semantics-based automatic evaluation metric Meteor is significantly correlated with all measures of cognitive effort considered. Smaller effects are also observed for source-text linguistic features. Further insight is provided into the role of the source text in post-editing, with results suggesting that consulting the source text is only associated with how cognitively demanding the task is perceived in the case of those with a low level of proficiency in the source language. Subjects' working memory capacity was also taken into account and a relationship with post-editing productivity could be noticed. Scaled-up studies into the construct of working memory capacity and the use of eye tracking in models for quality estimation are suggested as future work.
%0 Journal Article
%1 Vieira2014
%A Vieira, Lucas Nunes
%D 2014
%J Machine Translation
%K postedición traducción_automática
%N 3-4
%P 187-216
%R 10.1007/s10590-014-9156-x
%T Indices of cognitive effort in machine translation post-editing
%U http://link.springer.com/article/10.1007\%2Fs10590-014-9156-x
%V 28
%X Identifying indices of effort in post-editing of machine translation can have a number of applications, including estimating machine translation quality and calculating post-editors' pay rates. Both source-text and machine-output features as well as subjects' traits are investigated here in view of their impact on cognitive effort, which is measured with eye tracking and a subjective scale borrowed from the field of Educational Psychology. Data is analysed with mixed-effects models, and results indicate that the semantics-based automatic evaluation metric Meteor is significantly correlated with all measures of cognitive effort considered. Smaller effects are also observed for source-text linguistic features. Further insight is provided into the role of the source text in post-editing, with results suggesting that consulting the source text is only associated with how cognitively demanding the task is perceived in the case of those with a low level of proficiency in the source language. Subjects' working memory capacity was also taken into account and a relationship with post-editing productivity could be noticed. Scaled-up studies into the construct of working memory capacity and the use of eye tracking in models for quality estimation are suggested as future work.
%@ 0922-6567
@article{Vieira2014,
abstract = {Identifying indices of effort in post-editing of machine translation can have a number of applications, including estimating machine translation quality and calculating post-editors' pay rates. Both source-text and machine-output features as well as subjects' traits are investigated here in view of their impact on cognitive effort, which is measured with eye tracking and a subjective scale borrowed from the field of Educational Psychology. Data is analysed with mixed-effects models, and results indicate that the semantics-based automatic evaluation metric Meteor is significantly correlated with all measures of cognitive effort considered. Smaller effects are also observed for source-text linguistic features. Further insight is provided into the role of the source text in post-editing, with results suggesting that consulting the source text is only associated with how cognitively demanding the task is perceived in the case of those with a low level of proficiency in the source language. Subjects' working memory capacity was also taken into account and a relationship with post-editing productivity could be noticed. Scaled-up studies into the construct of working memory capacity and the use of eye tracking in models for quality estimation are suggested as future work.},
added-at = {2017-01-05T16:28:03.000+0100},
author = {Vieira, Lucas Nunes},
biburl = {https://www.bibsonomy.org/bibtex/2001511655a52c9aeff8262de6df8a3e0/coral.diez},
doi = {10.1007/s10590-014-9156-x},
interhash = {a2103c8edd52022310619493535a244c},
intrahash = {001511655a52c9aeff8262de6df8a3e0},
isbn = {0922-6567},
journal = {Machine Translation},
keywords = {postedición traducción_automática},
number = {3-4},
pages = {187-216},
timestamp = {2018-05-28T22:02:20.000+0200},
title = {Indices of cognitive effort in machine translation post-editing},
url = {http://link.springer.com/article/10.1007{\%}2Fs10590-014-9156-x},
volume = 28,
year = 2014
}