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 …

In recent years some researchers have explored the use of reinforcement learning (RL)
algorithms as key components in the solution of various natural language processing (NLP) …

Human evaluation of modern high-quality machine translation systems is a difficult problem,
and there is increasing evidence that inadequate evaluation procedures can lead to …

Advancements in deep neural networks have allowed automatic speech recognition (ASR)
systems to attain human parity on several publicly available clean speech datasets …

Pre-training and fine-tuning, eg, BERT, have achieved great success in language
understanding by transferring knowledge from rich-resource pre-training task to the low/zero …

This paper presents the results of the premier shared task organized alongside the
Conference on Machine Translation (WMT) 2019. Participants were asked to build machine …

The quality of human translation was long thought to be unattainable for computer
translation systems. In this study, we present a deep-learning system, CUBBITT, which …

We introduce an architecture to learn joint multilingual sentence representations for 93
languages, belonging to more than 30 different families and written in 28 different scripts …

We present a survey covering the state of the art in low-resource machine translation (MT)
research. There are currently around 7,000 languages spoken in the world and almost all …

Fine-tuning pre-trained Neural Machine Translation (NMT) models is the dominant approach
for adapting to new languages and domains. However, fine-tuning requires adapting and …