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 advent of neural networks, the translation quality surpasses that of the translations obtained using statistical techniques  . Up until three years ago, all neural translation models translated sentences independently, without incorporating any extra-sentential information . The aim of this  paper is to highlight the major works that have been undertaken in the space of document-level machine translation before and after the neural revolution  so that researchers can recognise where we started from and which direction we are heading in. When talking about the literature in statistical machine translation (SMT), we focus on works which have tried to improve the translation of specific discourse phenomena, while in neural machine translation (NMT), we focus on works which use the wider context explicitly . In addition to this, we also cover the evaluation strategies that have been introduced to account for the improvements in this domain .
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 URLanisation 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 .
R
advent
resurgence
A
for most language-pairs
R
three
a few
R
all
almost all of the
R
any extra-sentential information
the wider document-context and inter-dependencies among the sentences
A
survey
D
before and
A
,
R
where we started from and which direction we are heading in. When talking about the literature in statistical machine translation (SMT), we focus on works which have tried to improve the translation of specific discourse phenomena, while in neural machine translation (NMT), we focus on works which use the wider context explicitly
the current state and future directions of this field. We provide URLanisation of the literature based on novelties in modelling and architectures as well as training and decoding strategies
R
to this, we also cover the
, we cover
R
this domain
document MT, including automatic metrics and discourse-targeted test sets. We conclude by presenting possible avenues for future exploration in this research field