Data-driven Asian Adapted MQM Typology and Automation in Translation Quality Workflows

Abstract

In this study we aim to test the impact of applying translation error taxonomies oriented towards European Languages in the annotation of Asian Languages. We aim to demonstrate how an error typology adapted for the latter languages can not only result in more linguistically accurate annotations, but also how this can be applied to automating and scaling translation quality evaluation.
 As such, we propose a Translation Errors Typology that aims to cover the shortcomings of the Multidimensional Quality Metrics (Lommel et al. 2014) framework (MQM) in what concerns the annotation of the East Asian Languages of Mandarin, Japanese and Korean. The effectiveness of the typology here proposed was tested by analysing the Inter-annotator agreement (IAA) scores obtained, in contrast with the typology proposed by Ye and Toral (2020) and the Unbabel Error Typology1. Finally, we propose a way of automating Translation Quality Workflows through a Quality Estimation (QE) technology that is able to predict the MQM scores of machine translation outputs at scale with a fair correlation with the human judgement produced by applying the East Asian Languages MQM module proposed in this study.