  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 resurgence of neural networks, the translation quality
surpasses that of the translations obtained using statistical techniques for
most language-pairs. Up until a few years ago, almost all of the neural
translation models translated sentences independently, without incorporating
the wider document-context and inter-dependencies among the sentences. The aim
of this survey paper is to highlight the major works that have been undertaken
in the space of document-level machine translation after the neural revolution,
so that researchers can recognise the current state and future directions of
this field. We provide an organisation of the literature based on novelties in
modelling and architectures as well as training and decoding strategies. In
addition, we cover evaluation strategies that have been introduced to account
for the improvements in document MT, including automatic metrics and
discourse-targeted test sets. We conclude by presenting possible avenues for
future exploration in this research field.
