Machine translation post-editing is becoming commonplace and professional translators are often faced with this unknown task with little training and support. Given the different translation processes involved during post-editing, research suggests that untrained translators do not necessarily make good post-editors. Besides, the post-editing activity will be largely influenced by numerous aspe...

Many tasks, including driving, require frequent shifts in visual attention. Some visual shifting of attention is due to distraction from a secondary task, or from non-task relevant stimuli in the environment. Older people (e.g. over 60) are generally slower to react to stimuli and less skillful in learning to use new technologies. In this paper we are interested in studying the effect of age on...

With the noticeable improvement of the overall quality of Machine Translation (MT) systems in recent years, post-editing of MT output is starting to become a common practice among human translators. However, it is well known that the quality of a given MT system can vary significantly across translation segments and that post-editing bad quality translations is a tedious task that may require m...

Enforcement of global cooperation urges the rapid growth of cooperative work. As a typical application of it, cooperative editing is widely used in many fields gradually, especially in knowledge intensive application. Workflow is a kind of automation technology by predefined process rules and tasks. It can bring us clarity in process control and management, which are two important facets in coo...

Editing allows the selection of a representative subset of prototypes among the training sample to improve the performance of a classi cation task. The Wilson's editing algorithm was the rst proposal and then a great variety of new editing techniques have been proposed based on it. This algorithm consists on the elimination of prototypes in the training set that are misclassi ed using the k-NN ...

This work describes the AMU-UEdin submission to the WMT 2017 shared task on Automatic Post-Editing. We explore multiple neural architectures adapted for the task of automatic post-editing of machine translation output. We focus on neural end-to-end models that combine both inputsmt and src in a single neural architecture, modeling {mt, src} → pe directly. Apart from that, we investigate the inf...

This paper studies the minimum score at which machine translation (MT) evaluation metrics report productivity gains in a machine translation post-editing (MTPE) task. We ran an experiment involving 10 professional in-house translators from our company in which they were asked to carry out a real translation task involving MTPE, translation from scratch and fuzzymatch editing. We then analyzed t...

In the today’s information society, where information access is ubiquitous and data is abundantly available, the importance of memorization is giving way to information gathering skills (search, filtering, summarization), and this focus should also be reflected in the educational process. In this study, we focus on the problem-solving skills of information searching and editing (search-editing ...

the purpose of the present study is to develop appropriate scoring scales for each of the defined stages of the writing process, and also to determine to what extent these scoring scales can reliably and validly assess the performances of efl learners in an academic writing task. two hundred and two students’ writing samples were collected after a step-by-step process oriented essay writing ins...