Dr Özlem Temizöz

The advent of AI-supported, cloud-based collaborative translation platforms has enabled a new form of online collaborative translation — ‘concurrent translation’ (CT). CT refers to commercial translation performed on such platforms by multiple agents (translators, editors, subject-matter experts, etc.) simultaneously, via concurrent access. Although the practice has recently gained more ground, research on CT is scarce. The present article reports on selected key findings of a study that investigates translators’ experiences with CT via a survey of 804 professional translators working in CT mode across different commercial platforms. Despite the affordances such as peer learning, positive competition, speed, flexibility of the volume of work and working time, and reduced responsibility and reduced stress, CT workflow comes with its substantial challenges such as time pressure, negative competition, reduced self-revision and research, all of which result in quality compromised for speed.

The optimal approach to establishing communication between healthcare providers and
patients who do not share the same language has been discussed for quite some time; the
debates have ultimately served to highlight the crucial role that linguistically and culturally
competent human interpreters play in these instances of language-discordant interpersonal
healthcare communication.
However, there are many barriers to accessing human interpreters in healthcare settings,
including waiting times, lack of financial resources, restricted local availability of qualified/
trained interpreters in some languages and, at times, a lack of practicability ( for instance,
spontaneous communication in a hospital ward). Alternative approaches to accessing human
interpreters and approaches to providing language support without the need for an
interpreter has been developed, which involves drawing on and interacting with different
types of technology. They range from the use of audio and video communication tools enabling
remote interpretation to volunteer language support through crowdsourcing via digital
platforms, machine translation ( MT), and bespoke translation tools/ apps. Some of these
approaches were initially developed for situations of medical emergency and/ or humanitarian
crisis but have subsequently been explored in other settings of interpersonal healthcare
communication, shifting the patterns of interaction in these settings from human- to- human
interaction towards increasing involvement of human-machine interaction.
This chapter will review technology-assisted approaches to facilitating language-discordant
interpersonal healthcare communication and explore the extent to which they
are currently capable of meeting patients’ and healthcare providers’ communication needs.
The focus is on situations of dialogic communication and interaction normally involving an
interpreter, a patient and/ or caregiver and one or more healthcare providers. Technology-assisted
methods of translating written texts are not included ( on the latter, see Dew et al.
2018; Haddow, Birch and Heafield 2021; Vieira, O’Hagan and O’Sullivan 2021). Following
a brief epistemological consideration of technology use in interpersonal healthcare settings,
each approach will be considered in a separate section, beginning with a review of the relevant
scholarly literature and main practical developments, followed by a discussion of critical
issues and challenges arising.

This study compares the quality of postediting performed by
subject-matter experts as opposed to professional translators. A
total of 10 professional translators and 10 engineers postedited a
482-word technical text pre-translated from English into Turkish
using data-based machine translation system, Google Translate.
The findings suggest that, for this particular task (technical
translation), translators’ and engineers’ postediting quality is
similar as far as the categories of mistranslation, accuracy, and
consistency are concerned. Engineers performed significantly
better than translators only in the terminology category. In the
language category, translators made significantly fewer (minor)
errors than engineers. The qualitative data analysis revealed that,
for this particular task, a degree in translation does not directly
correlate with postediting quality, unless it is combined with
subject-matter knowledge and professional experience in
translation. Finally, the present study indicates that – both for the
engineers and the professional translators – expertise and
experience in the subject matter are important factors
determining postediting quality.

Counting and not counting recurring errors are two different methods that have been employed in translation quality evaluation without paying due attention to how the difference between the results of each method, if any, affects the quality score of the end product, thereby affecting the validity of the quality evaluation method in question. This paper reports on a study which shows that penalizing or not penalizing recurring errors in the target text significantly affects the quality score. The results reveal a need for a more critical approach in handling recurring errors in translation quality evaluation.

The paper reports on a pilot study conducted to test the methodology to
replicate the study by Jensen & Pavlović (2009) which investigates the effect
of translation directionality on cognitive processing by means of eye-tracking.
The following hypotheses are retested: (1) In both directions of translation,
processing the TT requires more cognitive effort than processing the ST;
(2) L2 translation tasks on the whole require more cognitive effort than L1
tasks; (3) cognitive effort invested in the processing of the ST is higher in
L1 translation than in L2 translation; (4) cognitive effort invested in the
processing of the TT is higher in L2 translation than in L1 translation. The
results showed that the findings of three out of four hypotheses were the
same as the findings of Jensen & Pavlović (2009). Both studies suggest that
neither processing the texts in L2 (ST or TT) nor translation into L2 leads to
a higher amount of cognitive effort. The findings are important in that they
challenge the traditional view of directionality that is based on traditional
assumptions rather than empirical data. This pilot study is distinctive in that
it is the first study in Turkey that uses eye-tracking to explore the translation
process (Temizöz 2009).

In parallel with the rise of MT and the integration of machine-translated segments into the translation workflow as TM input, empirical research on MT has gained momentum from the turn of the new millennium. This report covers empirical studies on machine translation and the postediting of MT output. It includes a synoptic table giving the author and year of each experiment, the number of participants, brief information on the participant profile, the type of text used, the number of words in the texts, language pair and direction used, and the name of the machine translation and/or translation memory system used.