Neural Machine Translation (NMT) has seen tremendous growth in the last ten years since
the early 2000s and has already entered a mature phase. While considered the most widely …

Abstract Machine translation (MT) is an important sub-field of natural language processing
that aims to translate natural languages using computers. In recent years, end-to-end neural …

As the capabilities of generative language models continue to advance, the implications of
biases ingrained within these models have garnered increasing attention from researchers …

An effective method to improve neural machine translation with monolingual data is to
augment the parallel training corpus with back-translations of target language sentences …

The development of neural techniques has opened up new avenues for research in
machine translation. Today, neural machine translation (NMT) systems can leverage highly …

The development of deep learning techniques has allowed Neural Machine Translation
(NMT) models to become extremely powerful, given sufficient training data and training time …

Abstract Machine translation is an important and challenging task that aims at automatically
translating natural language sentences from one language into another. Recently …

We present an approach to neural machine translation (NMT) that supports multiple
domains in a single model and allows switching between the domains when translating. The …

Transfer learning has been proven as an effective technique for neural machine translation
under low-resource conditions. Existing methods require a common target language …

Recent studies have demonstrated that pre-trained cross-lingual models achieve impressive
performance in downstream cross-lingual tasks. This improvement benefits from learning a …