Abstract Neural machine translation (NMT) is an approach to (MT) that uses deep learning techniques, a broad area of based on artificial neural networks (NNs). The book Machine Translation by Philipp Koehn targets range readers including researchers, scientists, academics, advanced undergraduate or postgraduate students, and users MT, covering wider topics fundamental network-based techniques m...

Plagiarism which is defined as “the wrongful appropriation of other writers’ or authors’ works and ideas without citing or informing them” poses a major challenge to knowledge spread publication. Plagiarism has been placed in four categories of direct, paraphrasing (rewriting), translation, and combinatory. This paper addresses translational plagiarism which is sometimes referred to as cross-li...

This paper reports ongoing experiments towards exploiting the use of images to provide additional context for statistical machine translation (SMT). We investigate whether this contextual information can be helpful in targeting two well-known challenges in machine translation: ambiguity (incorrect translation of words that have multiple senses) and out-of-vocabulary words (words left untranslat...

Abstract Literary occasionalisms, new words coined by writers with a particular poetic aim in view, often pose great challenge for translators. Given recent advances machine translation (MT), could literary translators benefit from MT when it comes to the of occasionalisms? We address this question considering work Austria’s most important nineteenth-century comedy writer, Johann Nestroy (1801–...