373 citations · 666 across the 8 of their papers we have counts for
9 papers
SeamlessM4T: Massively Multilingual & Multimodal Machine Translation
Seamless Communication, Loïc Barrault, Yu-An Chung +65
What does it take to create the Babel Fish, a tool that can help individuals translate speech between any two languages? While recent breakthroughs in text-based models have pushed…
Towards Being Parameter-Efficient: A Stratified Sparsely Activated Transformer with Dynamic Capacity
Haoran Xu, Maha Elbayad, Kenton Murray +2
Mixture-of-experts (MoE) models that employ sparse activation have demonstrated effectiveness in significantly increasing the number of parameters while maintaining low computation…
Language-Aware Multilingual Machine Translation with Self-Supervised Learning
Haoran Xu, Jean Maillard, Vedanuj Goswami
Multilingual machine translation (MMT) benefits from cross-lingual transfer but is a challenging multitask optimization problem. This is partly because there is no clear framework…
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,…
OCR Improves Machine Translation for Low-Resource Languages
Oana Ignat, Jean Maillard, Vishrav Chaudhary +1
We aim to investigate the performance of current OCR systems on low resource languages and low resource scripts. We introduce and make publicly available a novel benchmark, OCR4MT,…
Multi-task Retrieval for Knowledge-Intensive Tasks
Jean Maillard, Vladimir Karpukhin, Fabio Petroni +4
Retrieving relevant contexts from a large corpus is a crucial step for tasks such as open-domain question answering and fact checking. Although neural retrieval outperforms traditi…
Latent Tree Learning with Differentiable Parsers: Shift-Reduce Parsing and Chart Parsing
Jean Maillard, Stephen Clark
Latent tree learning models represent sentences by composing their words according to an induced parse tree, all based on a downstream task. These models often outperform baselines…
Jointly Learning Sentence Embeddings and Syntax with Unsupervised Tree-LSTMs
Jean Maillard, Stephen Clark, Dani Yogatama
We introduce a neural network that represents sentences by composing their words according to induced binary parse trees. We use Tree-LSTM as our composition function, applied alon…
Quantum discord bounds the amount of distributed entanglement
T. K. Chuan, J. Maillard, K. Modi +3
The ability to distribute quantum entanglement is a prerequisite for many fundamental tests of quantum theory and numerous quantum information protocols. Two distant parties can in…