Deep Neural Networks (DNNs) trained on one dataset (source domain) do not perform well
on another set of data (target domain), which is different but has similar properties as the …

Deep learning has produced state-of-the-art results for a variety of tasks. While such
approaches for supervised learning have performed well, they assume that training and …

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

Language models are generally trained on data spanning a wide range of topics (eg, news,
reviews, fiction), but they might be applied to an a priori unknown target distribution (eg …

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 present an approach to neural machine translation (NMT) that supports multiple
domains in a single model and allows switching between the domains when translating. The …

We share the findings of the first shared task on improving robustness of Machine
Translation (MT). The task provides a testbed representing challenges facing MT models …

Measuring domain relevance of data and identifying or selecting well-fit domain data for
machine translation (MT) is a well-studied topic, but denoising is not yet. Denoising is …

It has been previously noted that neural machine translation (NMT) is very sensitive to
domain shift. In this paper, we argue that this is a dual effect of the highly lexicalized nature …

With great practical value, the study of Multi-domain Neural Machine Translation (NMT)
mainly focuses on using mixed-domain parallel sentences to construct a unified model that …