Machine translation is used in many applications in everyday life. Due to the increase of
translated documents that need to be organized as useful or not (for building a translation …

Nowadays machine translation is widely used, but the required data for training, tuning and
testing a machine translation engine is often not sufficient or not useful. The automatic …

The automatic evaluation of machine translation (MT) has proven to be a very significant
research topic. Most automatic evaluation methods focus on the evaluation of the output of …

Translated texts are distinctively different from original ones, to the extent that supervised
text classification methods can distinguish between them with high accuracy. These …

Deep neural models tremendously improved machine translation. In this context, we
investigate whether distinguishing machine from human translations is still feasible. We …

We present the system we developed to provide efficient large-scale feature-rich
discriminative training for machine translation. We describe how we integrate with …

With a large amount of parallel data, neural machine translation systems are able to deliver
human-level performance for sentence-level translation. However, it is costly to label a large …

Today, access to the Internet provides access to various forms of knowledge like free online
lecture series offered by prestigious universities, massive open online courses, films and …

A language independent deep learning (DL) architecture for machine translation (MT)
evaluation is presented. This DL architecture aims at the best choice between two MT (S1 …

The translation quality of machine translation systems depends on the parallel corpus used
for training, including the quantity and quality of the corpus. However, building a highquality …