284 citations · 1.1k across the 20 of their papers we have counts for
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High Quality Rather than High Model Probability: Minimum Bayes Risk Decoding with Neural Metrics
Markus Freitag, David Grangier, Qijun Tan +1
In Neural Machine Translation, it is typically assumed that the sentence with the highest estimated probability should also be the translation with the highest quality as measured…
Scaling Laws for Neural Machine Translation
Behrooz Ghorbani, Orhan Firat, Markus Freitag +5
We present an empirical study of scaling properties of encoder-decoder Transformer models used in neural machine translation (NMT). We show that cross-entropy loss as a function of…
On Systematic Style Differences between Unsupervised and Supervised MT and an Application for High-Resource Machine Translation
Kelly Marchisio, Markus Freitag, David Grangier
Modern unsupervised machine translation (MT) systems reach reasonable translation quality under clean and controlled data conditions. As the performance gap between supervised and…
Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation
Markus Freitag, George Foster, David Grangier +3
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 erron…
Assessing Reference-Free Peer Evaluation for Machine Translation
Sweta Agrawal, George Foster, Markus Freitag +1
Reference-free evaluation has the potential to make machine translation evaluation substantially more scalable, allowing us to pivot easily to new languages or domains. It has been…