Abstract
Characteristics of the translation product are often used in translation process research as predictors for cognitive load, and by extension translation difficulty. In the last decade, user-activity information such as eye-tracking data has been increasingly employed as an experimental tool for that purpose. In this paper, we take a similar approach. We look for significant effects that different predictors may have on three different eye-tracking measures: First Fixation Duration (duration of first fixation on a token), Eye-Key Span (duration between first fixation on a token and the first keystroke contributing to its translation), and Total Reading Time on source tokens (sum of fixations on a token). As predictors we make use of a set of established metrics involving (lexico)semantics and word order, while also investigating the effect of more recent ones concerning syntax, semantics or both. Our results show a, particularly late, positive effect of many of the proposed predictors, suggesting that both fine-grained metrics of syntactic phenomena (such as word reordering) as well as coarse-grained ones (encapsulating both syntactic and semantic information) contribute to translation difficulties. The effect on especially late measures may indicate that the linguistic phenomena that our metrics capture (e.g., word reordering) are resolved in later stages during cognitive processing such as problem-solving and revision.
Highlights
Translation difficulty prediction, which aims to assess the difficulty of a translation task, is a topic of interest within Translation Studies that can benefit both pedagogical and research settings
“ANOVA” compares for each model whether it significantly improved over the previous model. “base” indicates when a model has been used as the first reference model in an ANOVA
The effect of HTra and absolute Cross on First Fixation Duration (FFDur) as reported in Schaeffer et al (2016b) could not be reproduced, HSTC was significant without outliers, which is interesting because it contains both reordering and translation entropy. word_cross was significant both with and without outliers but again, the variance explained was very small
Summary
Translation difficulty prediction, which aims to assess the difficulty of a translation task, is a topic of interest within Translation Studies that can benefit both pedagogical and research settings. The PreDicT project (Predicting Difficulty in Translation) aims to contribute to the field of translatability by investigating source text language features that add to a text’s translation difficulty. The application of advances in this field could be to predict the translation difficulty of a source text, or parts of it, without having access to a translation. In previous work (Vanroy et al, 2019), two metrics were introduced to calculate the word and word group movement on the sentence level. Additional sentence-level metrics were introduced in Vanroy et al (2021). We take a more fine-grained approach and make these metrics available on the word level so that meaningful translation process analyses can be done to investigate their impact on the translation task
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