Improving the translation environment for professional translators

When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side. This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project

A human evaluation of English-Irish statistical and neural machine translation

With official status in both Ireland and the EU, there is a need for high-quality English-Irish (EN-GA) machine translation (MT) systems which are suitable for use in a professional translation environment. While we have seen recent research on improving both statistical MT and neural MT for the EN-GA pair, the results of such systems have always been reported using automatic evaluation metrics. This paper provides the first human evaluation study of EN-GA MT using professional translators and in-domain (public administration) data for a more accurate depiction of the translation quality available via MT

The psychology of sustainability and psychological capital: new lenses to examine well-being in the translation profession.

The psychology of sustainability and sustainable development is a new research area which involves optimizing and regenerating personal resources in order to establish meaningful lives and work experiences (Di Fabio, 2017). The concept of psychological capital (PsyCap) has been linked to the psychology of sustainability due to its potential role in promoting well-being in organizations and improving people’s health and performance. However, this area of sustainability science is currently absent from the translation studies literature despite its relevance for professional translators’ work in today’s challenging and competitive environment. In line with the argument that sustainability principles apply to a variety of disciplines, the purpose of this article is to introduce the value of the psychology of sustainability for organizational and individual well-being via key concepts of relevance to the translation profession. Within this trans-disciplinary reflection space (Di Fabio and Rosen, 2018), I will also consider the research evidence for adopting a primary prevention perspective for the benefit of professional translators. Examining translator behaviour through the lens of the psychology of sustainability is a new and exciting venture that has the potential to reframe professional perspectives and translators’ career paths

Next generation translation and localization: Users are taking charge

Nonprofit translation activity driven by users and volunteer translators now represent a market force that easily rivals the mainstream translation and localization industries. While they still try to understand the drivers behind this nonprofit movement and occasionally attempt to tap in to these newly discovered “resources”, nonprofit translation efforts for good causes are growing at a phenomenal rate. This paper examines the case of The Rosetta Foundation as an example of a not-for-profit volunteer translation facilitator. The paper focuses on the motivating factors for volunteer translators. A survey was distributed to the several hundred volunteers who signed up as translators in the first few months of The Rosetta Foundation’s launch. The paper provides some background on what might well become the next generation of translation and localization and present the results of the survey. Finally, we will explore how The Rosetta Foundation, and other not-for-profit translation organisations might better motivate volunteers to contribute their skills and expertise

Improving the post-editing experience using translation recommendation: a user study

We report findings from a user study with professional post-editors using a translation recommendation framework (He et al., 2010) to integrate Statistical Machine Translation (SMT) output with Translation Memory (TM) systems. The framework recommends SMT outputs to a TM user when it predicts that SMT outputs are more suitable for post-editing than the hits provided by the TM. We analyze the effectiveness of the model as well as the reaction of potential users. Based on the performance statistics and the users’comments, we find that translation recommendation can reduce the workload of professional post-editors and improve the acceptance of MT in the localization industry

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

Towards a corpus-based, statistical approach of translation quality : measuring and visualizing linguistic deviance in student translations

In this article we present a corpus-based statistical approach to measuring translation quality, more particularly translation acceptability, by comparing the features of translated and original texts. We discuss initial findings that aim to support and objectify formative quality assessment. To that end, we extract a multitude of linguistic and textual features from both student and professional translation corpora that consist of many different translations by several translators in two different genres (fiction, news) and in two translation directions (English to French and French to Dutch). The numerical information gathered from these corpora is exploratively analysed with Principal Component Analysis, which enables us to identify stable, language-independent linguistic and textual indicators of student translations compared to translations produced by professionals. The differences between these types of translation are subsequently tested by means of ANOVA. The results clearly indicate that the proposed methodology is indeed capable of distinguishing between student and professional translations. It is claimed that this deviant behaviour indicates an overall lower translation quality in student translations: student translations tend to score lower at the acceptability level, that is, they deviate significantly from target-language norms and conventions. In addition, the proposed methodology is capable of assessing the acceptability of an individual student’s translation – a smaller linguistic distance between a given student translation and the norm set by the professional translations correlates with higher quality. The methodology is also able to provide objective and concrete feedback about the divergent linguistic dimensions in their text

Towards predicting post-editing productivity

Machine translation (MT) quality is generally measured via automatic metrics, producing scores that have no meaning for translators who are required to post-edit MT output or for project managers who have to plan and budget for transla- tion projects. This paper investigates correlations between two such automatic metrics (general text matcher and translation edit rate) and post-editing productivity. For the purposes of this paper, productivity is measured via processing speed and cognitive measures of effort using eye tracking as a tool. Processing speed, average fixation time and count are found to correlate well with the scores for groups of segments. Segments with high GTM and TER scores require substantially less time and cognitive effort than medium or low-scoring segments. Future research involving score thresholds and confidence estimation is suggested