2.6k citations · 5k across the 50 of their papers we have counts for
7 papers · 1 filter
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…
On the Complementarity of Data Selection and Fine Tuning for Domain Adaptation
Dan Iter, David Grangier
Domain adaptation of neural networks commonly relies on three training phases: pretraining, selected data training and then fine tuning. Data selection improves target domain gener…
The Trade-offs of Domain Adaptation for Neural Language Models
David Grangier, Dan Iter
This work connects language model adaptation with concepts of machine learning theory. We consider a training setup with a large out-of-domain set and a small in-domain set. We der…
Auxiliary Task Update Decomposition: The Good, The Bad and The Neutral
Lucio M. Dery, Yann Dauphin, David Grangier
While deep learning has been very beneficial in data-rich settings, tasks with smaller training set often resort to pre-training or multitask learning to leverage data from other t…
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…
DIVE: End-to-end Speech Diarization via Iterative Speaker Embedding
Neil Zeghidour, Olivier Teboul, David Grangier
We introduce DIVE, an end-to-end speaker diarization algorithm. Our neural algorithm presents the diarization task as an iterative process: it repeatedly builds a representation fo…