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 …

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 …

Deep learning applications have been thriving over the last decade in many different
domains, including computer vision and natural language understanding. The drivers for the …

In humans, Attention is a core property of all perceptual and cognitive operations. Given our
limited ability to process competing sources, attention mechanisms select, modulate, and …

We present a survey covering the state of the art in low-resource machine translation (MT)
research. There are currently around 7,000 languages spoken in the world and almost all …

This paper presents the results of the WMT17 shared tasks, which included three machine
translation (MT) tasks (news, biomedical, and multimodal), two evaluation tasks (metrics and …

We present iterative back-translation, a method for generating increasingly better synthetic
parallel data from monolingual data to train neural machine translation systems. Our …

Recently, non-recurrent architectures (convolutional, self-attentional) have outperformed
RNNs in neural machine translation. CNNs and self-attentional networks can connect distant …

Neural machine translation (NMT) often suffers from the vulnerability to noisy perturbations
in the input. We propose an approach to improving the robustness of NMT models, which …

The end-to-end nature of neural machine translation (NMT) removes many ways of manually
guiding the translation process that were available in older paradigms. Recent work …