The field of machine translation (MT), the automatic translation of written text from one
natural language into another, has experienced a major paradigm shift in recent years …

In the context of a more linked and globalized society, the significance of proficient cross-
cultural communication has been increasing to a position of utmost importance. Language …

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

The fast advancement in machine translation models necessitates the development of
accurate evaluation metrics that would allow researchers to track the progress in text …

In Neural Machine Translation (and, more generally, conditional language modeling), the
generation of a target token is influenced by two types of context: the source and the prefix of …

While target-side monolingual data has been proven to be very useful to improve neural
machine translation (briefly, NMT) through back translation, source-side monolingual data is …

In this paper, we show that neural machine translation (NMT) systems trained on large back-
translated data overfit some of the characteristics of machine-translated texts. Such NMT …

Back-translation-data augmentation by translating target monolingual data-is a crucial
component in modern neural machine translation (NMT). In this work, we reformulate back …

One important issue that affects the performance of neural machine translation is the scale of
available parallel data. For low-resource languages, the amount of parallel data is not …

How to generate descriptions from structured data organized in tables? Existing approaches
using neural encoder-decoder models often suffer from lacking diversity. We claim that an …