Abstract
As Machine Translation (MT) becomes increasingly ubiquitous, so does its use in professional translation workflows. However, its proliferation in the translation industry has brought about new challenges in the field of Post-Editing (PE). We are now faced with a need to find effective tools to assess the quality of MT systems to avoid underpayments and mistrust by professional translators. In this scenario, one promising field of study is MT Quality Estimation (MTQE), as this aims to determine the quality of an automatic translation and, indirectly, its degree of post-editing difficulty. However, its impact on the translation workflows and the translators’ cognitive load is still to be fully explored. We report on the results of an impact study engaging professional translators in PE tasks using MTQE. To assess the translators’ cognitive load we measure their productivity both in terms of time and effort (keystrokes) in three different scenarios: translating from scratch, post-editing without using MTQE, and post-editing using MTQE. Our results show that good MTQE information can improve post-editing efficiency and decrease the cognitive load on translators. This is especially true for cases with low MT quality.
Highlights
A poor Machine Translation (MT) suggestion might end up taking the post-editor more time to assess and rewrite than it would to translate from scratch [3]. (For the purposes of this paper, we refer to post-editing as a task usually carried out by professional translators, and we use the term professional translators as an umbrella for both translators and post-editors.) Consistent low-quality MT can prompt professional translators to give up on post-editing and revert to translating from scratch
This paper investigates the effect of Machine Translation Quality Estimation (MTQE)
It has been shown in a number of studies that the use of MT by professional translators often leads to productivity gains which are, in turn, related to the quality of the MT segments provided to the translators [5,6]
Summary
Machine Translation Quality Estimation (MTQE) can provide this assessment and help the post-editor by only proposing sentences which are good enough to be post-edited. It has been shown in a number of studies that the use of MT by professional translators often leads to productivity gains which are, in turn, related to the quality of the MT segments provided to the translators [5,6]. “QE research has not been followed by conclusive results that demonstrate whether the use of quality labels can lead to noticeable productivity gains in the CAT framework” This suggests that how we use MTQE in the translation workflows is still an open question to be answered, despite previous work integrating MTQE into them. Turchi et al [7]
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