Large language models have been shown to achieve remarkable performance across a
variety of natural language tasks using few-shot learning, which drastically reduces the …

Existing work in translation demonstrated the potential of massively multilingual machine
translation by training a single model able to translate between any pair of languages …

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 …

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 …

We investigate the following question for machine translation (MT): can we develop a single
universal MT model to serve as the common seed and obtain derivative and improved …

We present a simple and effective pretraining strategy--bidirectional training (BiT) for neural
machine translation. Specifically, we bidirectionally update the model parameters at the …

Modern neural machine translation (NMT) models have achieved competitive performance
in standard benchmarks such as WMT. However, there still exist significant issues such as …

We propose a straightforward vocabulary adaptation scheme to extend the language
capacity of multilingual machine translation models, paving the way towards efficient …

Unsupervised neural machine translation (UNMT) has recently achieved remarkable results
for several language pairs. However, it can only translate between a single language pair …

Deep encoders have been proven to be effective in improving neural machine translation
(NMT) systems, but training an extremely deep encoder is time consuming. Moreover, why …