293 citations · 603 across the 10 of their papers we have counts for
13 papers · 1 filter
XTREME-S: Evaluating Cross-lingual Speech Representations
Alexis Conneau, Ankur Bapna, Yu Zhang +16
We introduce XTREME-S, a new benchmark to evaluate universal cross-lingual speech representations in many languages. XTREME-S covers four task families: speech recognition, classif…
mSLAM: Massively multilingual joint pre-training for speech and text
Ankur Bapna, Colin Cherry, Yu Zhang +6
We present mSLAM, a multilingual Speech and LAnguage Model that learns cross-lingual cross-modal representations of speech and text by pre-training jointly on large amounts of unla…
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…
Sentence Boundary Augmentation For Neural Machine Translation Robustness
Daniel Li, Te I, Naveen Arivazhagan +2
Neural Machine Translation (NMT) models have demonstrated strong state of the art performance on translation tasks where well-formed training and evaluation data are provided, but…
Human-Paraphrased References Improve Neural Machine Translation
Markus Freitag, George Foster, David Grangier +1
Automatic evaluation comparing candidate translations to human-generated paraphrases of reference translations has recently been proposed by Freitag et al. When used in place of or…
Inference Strategies for Machine Translation with Conditional Masking
Julia Kreutzer, George Foster, Colin Cherry
Conditional masked language model (CMLM) training has proven successful for non-autoregressive and semi-autoregressive sequence generation tasks, such as machine translation. Given…