The field of machine translation (MT), the automatic translation of written text from one
natural language into another, has experienced a major paradigm shift in recent years …

Overparameterized networks trained to convergence have shown impressive performance
in domains such as computer vision and natural language processing. Pushing state of the …

In recent years, deep neural networks have been successful in both industry and academia,
especially for computer vision tasks. The great success of deep learning is mainly due to its …

We formally study how ensemble of deep learning models can improve test accuracy, and
how the superior performance of ensemble can be distilled into a single model using …

Multilingual machine translation, which translates multiple languages with a single model,
has attracted much attention due to its efficiency of offline training and online serving …

Abstract Knowledge distillation is considered as a training and compression strategy in
which two neural networks, namely a teacher and a student, are coupled together during …

In recent years, the fields of natural language processing (NLP) and information retrieval (IR)
have made tremendous progress thanks to deep learning models like Recurrent Neural …

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

End-to-end speech translation (ST), which directly translates from source language speech
into target language text, has attracted intensive attentions in recent years. Compared to …

Abstract We introduce Data Diversification: a simple but effective strategy to boost neural
machine translation (NMT) performance. It diversifies the training data by using the …