Many subfields of machine learning share a common stumbling block: evaluation. Advances
in machine learning often evaporate under closer scrutiny or turn out to be less widely …

Generative Pre-trained Transformer (GPT) models have shown remarkable capabilities for
natural language generation, but their performance for machine translation has not been …

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 …

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 …

Automatic metrics are commonly used as the exclusive tool for declaring the superiority of
one machine translation system's quality over another. The community choice of automatic …

India has a rich linguistic landscape with languages from 4 major language families spoken
by over a billion people. 22 of these languages are listed in the Constitution of India …

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

The quality of automatic metrics for machine translation has been increasingly called into
question, especially for high-quality systems. This paper demonstrates that, while choice of …

We describe Facebook's multilingual model submission to the WMT2021 shared task on
news translation. We participate in 14 language directions: English to and from Czech …

Both human and machine translation play a central role in cross-lingual transfer learning:
many multilingual datasets have been created through professional translation services, and …