The ready availability of machine translation (MT) systems such as Google Translate has profoundly changed how society engages with multilingual communication practices. In addition to private use situations, this technology is now used to overcome language barriers in high-risk settings such as hospitals and courts. MT errors pose serious risks in environments like these, but there is little understanding of the nature of these risks and of the wider implications of using this technology. This article is the first structured study of the consequences of uninformed MT use in healthcare and law. Based on a critical literature review, the article presents a qualitative meta-analysis of official documents and published research on the use of MT in these two fields. Its findings prompt calls for action in three areas. First, the review shows that research on MT use in healthcare and law can often disregard the complexities of language and language translation. The article calls for cross-disciplinary research to address this gap by ensuring that a growing body of relevant knowledge in translation studies informs research conducted within the medical and legal sectors. Second, the review highlights a broad societal need for higher levels of awareness of the specific strengths and, crucially, of the limitations of MT. Finally, the article concludes that MT technology can in its current state exacerbate social inequalities and put certain communities of users at greater risk. We highlight this as a persistent issue that merits further attention from researchers and policymakers.

Translation is currently described as a profession under pressure from automation, falling prices and globalized competition. Translators' stance on machine translation (MT) is famously negative, but the economic dimension of this positioning is scarcely researched and often unclear. This article provides an analysis of translators' blog and forum postings contextualized within general trends in employment, the economy and work automation. The analysis concentrates on MT and pay. Two key findings are reported. First, MT was found to be a secondary issue in translators' comments on pay; most grievances were based on business practices themselves. Second, most criticisms of MT were rooted not in fears of being outperformed by MT systems, but rather in the technology's limitations and market consequences. This article calls for a broadening of translators' role across areas of specialization and argues that, in the debate on translation's future, MT cannot be decoupled from its economic effects.

There has been growing interest of late in the cognitive effort required by post-editing of machine translation. Compared to number of editing operations, cognitive (or mental) effort is frequently considered a more decisive indicator of the overall effort expended by post-editors. Estimating cognitive effort is not straightforward, however. Previous studies often triangulate different measures to obtain a consensus, but little post-editing research to date has attempted to show how measures of cognitive effort relate to each other in a multivariate analysis. This paper addresses this by presenting an exploratory comparison of cognitive measures based on eye tracking, pauses, editing time, and subjective ratings collected in a post-editing task carried out by professional and non-professional participants. All measures correlated with each other, but a principal components analysis showed that the measures cluster together in different ways. In particular, measures that increase with task time alone behaved differently from the others, with higher mutual associations and higher reliability. Regarding differences between professional and non-professional participants, it was observed that subjective ratings were overall more strongly associated with objective measures in the case of professionals. Surprising findings from previous research based on pause ratio are discussed. The paper argues that a pause typology will benefit the study of pause lengths and cognitive effort in post-editing.

Identifying indices of effort in post-editing of machine translation can have a number of applications, including estimating machine translation quality and calculating post-editors' pay rates. Both source-text and machine-output features as well as subjects' traits are investigated here in view of their impact on cognitive effort, which is measured with eye tracking and a subjective scale borrowed from the field of Educational Psychology. Data is analysed with mixed-effects models, and results indicate that the semantics-based automatic evaluation metric Meteor is significantly correlated with all measures of cognitive effort considered. Smaller effects are also observed for source-text linguistic features. Further insight is provided into the role of the source text in post-editing, with results suggesting that consulting the source text is only associated with how cognitively demanding the task is perceived in the case of those with a low level of proficiency in the source language. Subjects' working memory capacity was also taken into account and a relationship with post-editing productivity could be noticed. Scaled-up studies into the construct of working memory capacity and the use of eye tracking in models for quality estimation are suggested as future work.

This chapter analyses the evolutionary process that human post-editing of Machine Translation (MT) output has undergone in previous years. It is well known that post-editing can improve translating productivity as well as target-text quality relative to translation carried out 'from scratch', but postediting has many facets. Initially, it was akin to a step in the MT development pipeline where humans tidied the machine output to make it usable. More recently, post-editing is a professional service with its own international standard. In addition, the focus of translation tasks involving MT is shifting towards more integrated and human-centred environments. The chapter reviews previous postediting research and discusses emerging issues and future directions on the use of MT in professional translation. It outlines how post-editing has matured as a practice and service and adopts an encompassing understanding of post-editing tasks that reflects MT's changing role in professional translation processes.