Neural Machine Translation (NMT) has seen tremendous growth in the last ten years since
the early 2000s and has already entered a mature phase. While considered the most widely …

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 …

Abstract Machine translation (MT) is an important sub-field of natural language processing
that aims to translate natural languages using computers. In recent years, end-to-end neural …

Research on prompting has shown excellent performance with little or even no supervised
training across many tasks. However, prompting for machine translation is still under …

One of the biggest challenges hindering progress in low-resource and multilingual machine
translation is the lack of good evaluation benchmarks. Current evaluation benchmarks either …

As language models grow ever larger, the need for large-scale high-quality text datasets has
never been more pressing, especially in multilingual settings. The BigScience workshop, a 1 …

Hallucinated translations can severely undermine and raise safety issues when machine
translation systems are deployed in the wild. Previous research on the topic focused on …

We introduce MADLAD-400, a manually audited, general domain 3T token monolingual
dataset based on CommonCrawl, spanning 419 languages. We discuss the limitations …

Recent work demonstrates the potential of multilingual pretraining of creating one model that
can be used for various tasks in different languages. Previous work in multilingual …

In this paper, we propose a simple yet effective method to stabilize extremely deep
Transformers. Specifically, we introduce a new normalization function (DeepNorm) to modify …