Entropy as a measurement of cognitive load in translation

Yuxiang Wei


Abstract
In view of the “predictive turn” in translation studies, empirical investigations of the translation process have shown increasing interest in studying features of the text which can predict translation efficiency and effort, especially using large-scale experimental data and rigorous statistical means. In this regard, a novel metric based on entropy (i.e., HTra) has been proposed and experimentally studied as a predictor variable. On the one hand, empirical studies show that HTra as a product-based metric can predict effort, and on the other, some conceptual analyses have provided theoretical justifications of entropy or entropy reduction as a description of translation from a process perspective. This paper continues the investigation of entropy, conceptually examining two ways of quantifying cognitive load, namely, shift of resource allocation and reduction of entropy, and argues that the former is represented by surprisal and ITra while the latter is represented by HTra. Both can be approximated via corpus-based means and used as potential predictors of effort. Empirical analyses were also conducted comparing the two metrics (i.e., HTra and ITra) in terms of their prediction of effort, which showed that ITra is a stronger predictor for TT production time while HTra is a stronger predictor for ST reading time. It is hoped that this would contribute to the exploration of dependable, theoretically justifiable means of predicting the effort involved in translation
Anthology ID:
2022.amta-wetpr.8
Volume:
Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Workshop 1: Empirical Translation Process Research)
Month:
September
Year:
2022
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Venue:
AMTA
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Publisher:
Association for Machine Translation in the Americas
Note:
Pages:
75–86
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URL:
https://aclanthology.org/2022.amta-wetpr.8
DOI:
Bibkey:
Cite (ACL):
Yuxiang Wei. 2022. Entropy as a measurement of cognitive load in translation. In Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Workshop 1: Empirical Translation Process Research), pages 75–86, None. Association for Machine Translation in the Americas.
Cite (Informal):
Entropy as a measurement of cognitive load in translation (Wei, AMTA 2022)
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PDF:
https://preview.aclanthology.org/ingestion-script-update/2022.amta-wetpr.8.pdf