Machine translation (MT) is an important task in natural language processing (NLP), as it
automates the translation process and reduces the reliance on human translators. With the …

Existing work in translation demonstrated the potential of massively multilingual machine
translation by training a single model able to translate between any pair of languages …

This paper presents the results of the premier shared task organized alongside the
Conference on Machine Translation (WMT) 2019. Participants were asked to build machine …

Document-level context for neural machine translation (NMT) is crucial to improve the
translation consistency and cohesion, the translation of ambiguous inputs, as well as several …

We outline the Great Misalignment Problem in natural language processing research, this
means simply that the problem definition is not in line with the method proposed and the …

This work presents a decoding architecture that fuses the information from a neural
translation model and the context semantics enclosed in a semantic space language model …

In this thesis, we study multilingual neural machine translation (NMT) systems and the
representations they learn, with a focus on whether they encode significant semantic …

In this thesis, we study the automatic translation of documents by taking into account cross-
sentence phenomena. This document-level information is typically ignored by most of the …

Deep neural networks (DNN) and linguistic rules are currently the opposite ends in the scale
for NLP technologies. Until recently, it has not been known how to combine these …