Addressing Zero-Resource Domains Using Document-Level Context in Neural Machine Translation
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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.
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