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 …

We introduce our efforts towards building a universal neural machine translation (NMT)
system capable of translating between any language pair. We set a milestone towards this …

We describe an open-source toolkit for neural machine translation (NMT). The toolkit
prioritizes efficiency, modularity, and extensibility with the goal of supporting NMT research …

We propose a simple solution to use a single Neural Machine Translation (NMT) model to
translate between multiple languages. Our solution requires no changes to the model …

Current state-of-the-art NMT systems use large neural networks that are not only slow to
train, but also often require many heuristics and optimization tricks, such as specialized …

In this article, we perform a review of the state-of-the-art of hybrid machine learning in
medical imaging. We start with a short summary of the general developments of the past in …

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 …

Abstract Machine Translation (MT) has made significant progress in recent years using deep
learning, especially after the emergence of large language models (LLMs) such as GPT-3 …

This paper proposes a novel method to inject custom terminology into neural machine
translation at run time. Previous works have mainly proposed modifications to the decoding …

In this paper, we extend an attention-based neural machine translation (NMT) model by
allowing it to access an entire training set of parallel sentence pairs even after training. The …