Neural machine translation (NMT) is a deep learning based approach for machine
translation, which outperforms traditional statistical machine translation (SMT) and yields the …

Transfer learning is aimed to make use of valuable knowledge in a source domain to help
model performance in a target domain. It is particularly important to neural networks, which …

The development of deep learning techniques has allowed Neural Machine Translation
(NMT) models to become extremely powerful, given sufficient training data and training time …

We explore efficient domain adaptation for the task of statistical machine translation based
on extracting sentences from a large generaldomain parallel corpus that are most relevant to …

State-of-the-art abstractive summarization models generally rely on extensive labeled data,
which lowers their generalization ability on domains where such data are not available. In …

We introduce a curriculum learning approach to adapt generic neural machine translation
models to a specific domain. Samples are grouped by their similarities to the domain of …

Neural Machine Translation (NMT) is a new approach for automatic translation of text from
one human language into another. The basic concept in NMT is to train a large Neural …

We study the problem of learning domain invariant representations for time series data while
transferring the complex temporal latent dependencies between the domains. Our model …

Instance weighting has been widely applied to phrase-based machine translation domain
adaptation. However, it is challenging to be applied to Neural Machine Translation (NMT) …