373 citations · 604 across the 21 of their papers we have counts for
22 papers
Efficiently Upgrading Multilingual Machine Translation Models to Support More Languages
Simeng Sun, Maha Elbayad, Anna Sun +1
With multilingual machine translation (MMT) models continuing to grow in size and number of supported languages, it is natural to reuse and upgrade existing models to save computat…
No Language Left Behind: Scaling Human-Centered Machine Translation
NLLB Team, Marta R. Costa-jussà, James Cross +36
Driven by the goal of eradicating language barriers on a global scale, machine translation has solidified itself as a key focus of artificial intelligence research today. However,…
Multilingual Neural Machine Translation with Deep Encoder and Multiple Shallow Decoders
Xiang Kong, Adithya Renduchintala, James Cross +3
Recent work in multilingual translation advances translation quality surpassing bilingual baselines using deep transformer models with increased capacity. However, the extra latenc…
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…
Multilingual Machine Translation with Hyper-Adapters
Christos Baziotis, Mikel Artetxe, James Cross +1
Multilingual machine translation suffers from negative interference across languages. A common solution is to relax parameter sharing with language-specific modules like adapters.…
How Robust is Neural Machine Translation to Language Imbalance in Multilingual Tokenizer Training?
Shiyue Zhang, Vishrav Chaudhary, Naman Goyal +4
A multilingual tokenizer is a fundamental component of multilingual neural machine translation. It is trained from a multilingual corpus. Since a skewed data distribution is consid…