Achieving satisfying performance in machine translation on domains for which there is no training data is challenging. Traditional  domain adaptation is not suitable for addressing such zero-resource domains because it relies on in-domain parallel data. We show that document-level context can be used to capture domain generalities when in-domain parallel data is not available . We present two document-level Transformer models which are capable of using large context sizes and we compare these models against strong Transformer baselines. We obtain improvements for the two zero-resource domains we study. We additionally present experiments showing the usefulness of large context when modeling multiple domains at once .
Achieving satisfying performance in machine translation on domains for which there is no training data is challenging. Traditional supervised domain adaptation is not suitable for addressing such zero-resource domains because it relies on in-domain parallel data. We show that when in-domain parallel data is not available , access to document-level context enables better capturing of domain generalities compared to only having access to a single sentence. Having access to more information provides a more reliable domain estimation. We present two document-level Transformer models which are capable of using large context sizes and we compare these models against strong Transformer baselines. We obtain improvements for the two zero resource domains we study. We additionally provide an analysis where we vary the amount of context and look at the case where in-domain data is available .
A
supervised
D
document-level context can be used to capture domain generalities
R
.
, access to document-level context enables better capturing of domain generalities compared to only having access to a single sentence. Having access to more information provides a more reliable domain estimation.
R
zero-resource
zero resource
R
present experiments showing the usefulness of large context when modeling multiple domains at once
provide an analysis where we vary the amount of context and look at the case where in-domain data is available