A Bayesian approach to translators' reliability assessment

TL;DR

This paper introduces a Bayesian framework for assessing the reliability of translators and reviewers in translation quality evaluation, revealing insights into their skills, biases, and consistency even with limited data.

Contribution

It develops and validates two Bayesian models to quantify translator and reviewer reliability, providing a new quantitative approach to translation quality assessment.

Findings

Reviewers' reliability varies and can be biased by translator expertise.

Highly experienced translators show the highest consistency in translation and assessment.

Meaningful insights into translator skills can be obtained with only one review per translation.

Abstract

Translation Quality Assessment (TQA) is a process conducted by human translators and is widely used, both for estimating the performance of (increasingly used) Machine Translation, and for finding an agreement between translation providers and their customers. While translation scholars are aware of the importance of having a reliable way to conduct the TQA process, it seems that there is limited literature that tackles the issue of reliability with a quantitative approach. In this work, we consider the TQA as a complex process from the point of view of physics of complex systems and approach the reliability issue from the Bayesian paradigm. Using a dataset of translation quality evaluations (in the form of error annotations), produced entirely by the Professional Translation Service Provider Translated SRL, we compare two Bayesian models that parameterise the following features involved in the TQA process: the translation difficulty, the characteristics of the translators involved in producing the translation, and of those assessing its quality - the reviewers. We validate the models in an unsupervised setting and show that it is possible to get meaningful insights into translators even with just one review per translation; subsequently, we extract information like translators' skills and reviewers' strictness, as well as their consistency in their respective roles. Using this, we show that the reliability of reviewers cannot be taken for granted even in the case of expert translators: a translator's expertise can induce a cognitive bias when reviewing a translation produced by another translator. The most expert translators, however, are characterised by the highest level of consistency, both in translating and in assessing the translation quality.