Artificial Intelligence has gained a lot of popularity in recent years thanks to the advent of,
mainly, Deep Learning techniques. These algorithms have broken many of the barriers in …

Machine translation quality estimation (QE) predicts human judgements of a translation
hypothesis without seeing the reference. State-of-the-art QE systems based on pretrained …

Parallel corpora have been widely used in the fields of natural language processing and
translation as they provide crucial multilingual information. They are used to train machine …

Artificial intelligence (AI) systems are becoming wiser, even surpassing human
performances in some fields, such as image classification, chess, and Go. However, most …

Previous studies show that neural machine translation (NMT) systems produce translations
with higher quality when highly similar sentences (ie fuzzy matches; FMs) to a given input …

Quality estimation (QE) task aims to predict the machine translation (MT) quality well by
referring to the source sentence and its MT output. The various applicability of QE proves the …

Summarization has remained as an old idea of representing and expressing important facts
and ideas in a shorter format without distorting the main context of the content at which they …

Building of data for quality estimation (QE) training is expensive and requires significant
human labor. In this study, we focus on a data-centric approach while performing QE, and …

Automatic post editing is a research field that aims to automatically correct errors in machine
translation results. This research is mainly being focus on high resource language pairs …

With an increasing amount of data in the art world, discovering artists and artworks suitable
to collectors' tastes becomes a challenge. It is no longer enough to use visual information, as …