The Borrowers: Researching the cognitive aspects of translation

The paper considers the interdisciplinary interaction of research on the cognitive aspects of translation. Examples of influence from linguistics, psychology, neuroscience, cognitive science, reading and writing research and language technology are given, with examples from specific sub-disciplines within each one. The breadth of borrowing by researchers in cognitive translatology is made apparent, but the minimal influence of cognitive translatology on the respective disciplines themselves is also highlighted. Suggestions for future developments are made, including ways in which the domain of cognitive translatology might exert greater influence on other disciplines

Investigating the effects of controlled language on the reading and comprehension of machine translated texts: A mixed-methods approach

This study investigates whether the use of controlled language (CL) improves the readability and comprehension of technical support documentation produced by a statistical machine translation system. Readability is operationalised here as the extent to which a text can be easily read in terms of formal linguistic elements; while comprehensibility is defined as how easily a text’s content can be understood by the reader. A biphasic mixed-methods triangulation approach is taken, in which a number of quantitative and qualitative evaluation methods are combined. These include: eye tracking, automatic evaluation metrics (AEMs), retrospective interviews, human evaluations, memory recall testing, and readability indices. A further aim of the research is to investigate what, if any, correlations exist between the various metrics used, and to explore the cognitive framework of the evaluation process. The research finds that the use of CL input results in significantly higher scores for items recalled by participants, and for several of the eye tracking metrics: fixation count, fixation length, and regressions. However, the findings show slight insignificant increases for readability indices and human evaluations, and slight insignificant decreases for AEMs. Several significant correlations between the above metrics are identified as well as predictors of readability and comprehensibility

Eye tracking in translation process research: methodological challenges and solutions

Eye tracking has been eagerly adopted as a technique in translation process research in recent years and, with it, comes a long list of methodological challenges, some of which are specific to translation process research, some of which are more general. This paper represents an attempt by a recent convert to eye tracking to document the various challenges and to offer suggestions for how some of them might be addressed by others who are interested in embracing this very interesting mode of investigation. It is difficult to comment only on those challenges related to eye tracking without mentioning issues that pertain to the more general questions of research design in the domain of translation process research as the two are inevitably linked. By highlighting challenges, one is inevitably exposing research design weaknesses. However, this should not be viewed negatively but should rather be seen as a means of improving the quality of combined research outputs with the aim of maturing the domain. This paper is broadly divided into two parts: the first discusses the methodological challenges, which in turn are divided into the categories of research environment, research participants, ethics, data explosion and validity. Each section is then repeated in the second part of the paper, which offers suggestions on how the challenges might be addressed

Machine translation and Welsh: analysing free statistical machine translation for the professional translation of an under-researched language pair

This article reports on a key-logging study carried out to test the benefits of post-editing Machine Translation (MT) for the professional translator within a hypothetico-deductive framework, contrasting the outcomes of a number of variables which are inextricably linked to the professional translation process. Given the current trend of allowing the professional translator to connect to Google Translate services within the main Translation Memory (TM) systems via an API, a between-groups design is utilized in which cognitive, technical and temporal effort are gauged between translation and post-editing the statistical MT engine Google Translate. The language pair investigated is English and Welsh. Results show no statistical difference between post-editing and translation in terms of processing time. Using a novel measure of cognitive effort focused on pauses, the cognitive effort exerted by post-editors and translators was also found to be statistically different. Results also show however that a complex relationship exists between post-editing, translation and technical effort, in that aspects of text production processes were seen to be eased by post-editing. Finally, a bilingual review by two different translators found little difference in quality between the translated and post-edited texts, and that both sets of texts were acceptable according to accuracy and fidelity

Pre-editing and post-editing

This chapter provides an accessible introductory view of pre-editing and post-editing as the starting-point for research or work in the language industry. It describes source text pre-editing and machine translation post-editing from an industrial as well as academic point of view. In the last ten to fifteen years, there has been a considerable growth in the number of studies and publications dealing with pre-editing, and especially post-editing, that have helped researchers and the industry to understand the impact machine translation technology has on translators’ output and their working environment. This interest is likely to continue in view of the recent developments in neural machine translation and artificial intelligence. Although the latest technology has taken a considerable leap forward, the existing body of work should not be disregarded as it has defined clear research lines and methods, as it is more necessary than ever to look at data in their appropriate context and avoid generalizing in the vast and diverse territory of human and machine translation

Pre-editing and post-editing

This chapter provides an accessible introductory view of pre-editing and post-editing as the starting-point for research or work in the language industry. It describes source text pre-editing and machine translation post-editing from an industrial as well as academic point of view. In the last ten to fifteen years, there has been a considerable growth in the number of studies and publications dealing with pre-editing, and especially post-editing, that have helped researchers and the industry to understand the impact machine translation technology has on translators’ output and their working environment. This interest is likely to continue in view of the recent developments in neural machine translation and artificial intelligence. Although the latest technology has taken a considerable leap forward, the existing body of work should not be disregarded as it has defined clear research lines and methods, as it is more necessary than ever to look at data in their appropriate context and avoid generalizing in the vast and diverse territory of human and machine translation