68 citations · 94 across the 9 of their papers we have counts for
13 papers · 1 filter
Lifting the Curse of Multilinguality by Pre-training Modular Transformers
Jonas Pfeiffer, Naman Goyal, Xi Victoria Lin +4
Multilingual pre-trained models are known to suffer from the curse of multilinguality, which causes per-language performance to drop as they cover more languages. We address this i…
Data Selection Curriculum for Neural Machine Translation
Tasnim Mohiuddin, Philipp Koehn, Vishrav Chaudhary +3
Neural Machine Translation (NMT) models are typically trained on heterogeneous data that are concatenated and randomly shuffled. However, not all of the training data are equally u…
Tricks for Training Sparse Translation Models
Dheeru Dua, Shruti Bhosale, Vedanuj Goswami +3
Multi-task learning with an unbalanced data distribution skews model learning towards high resource tasks, especially when model capacity is fixed and fully shared across all tasks…
Alternative Input Signals Ease Transfer in Multilingual Machine Translation
Simeng Sun, Angela Fan, James Cross +4
Recent work in multilingual machine translation (MMT) has focused on the potential of positive transfer between languages, particularly cases where higher-resourced languages can b…
Classification-based Quality Estimation: Small and Efficient Models for Real-world Applications
Shuo Sun, Ahmed El-Kishky, Vishrav Chaudhary +3
Sentence-level Quality estimation (QE) of machine translation is traditionally formulated as a regression task, and the performance of QE models is typically measured by Pearson co…
Facebook AI WMT21 News Translation Task Submission
Chau Tran, Shruti Bhosale, James Cross +3
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, German, Ha…