activity
20212024
most citedNo Language Left Behind: Scaling Human-Centered Machine Translation

373 citations · 392 across the 8 of their papers we have counts for

collaborators

9 papers

cs.CL2024

FuxiTranyu: A Multilingual Large Language Model Trained with Balanced Data

Haoran Sun, Renren Jin, Shaoyang Xu +10

Large language models (LLMs) have demonstrated prowess in a wide range of tasks. However, many LLMs exhibit significant performance discrepancies between high- and low-resource lan…

cs.CL2023★ 14 cited

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…

cs.CL2023★ 1 cited

Hybrid Transducer and Attention based Encoder-Decoder Modeling for Speech-to-Text Tasks

Yun Tang, Anna Y. Sun, Hirofumi Inaguma +5

Transducer and Attention based Encoder-Decoder (AED) are two widely used frameworks for speech-to-text tasks. They are designed for different purposes and each has its own benefits…

cs.DC2023★ 4 cited

Towards MoE Deployment: Mitigating Inefficiencies in Mixture-of-Expert (MoE) Inference

Haiyang Huang, Newsha Ardalani, Anna Sun +6

Mixture-of-Experts (MoE) models have gained popularity in achieving state-of-the-art performance in a wide range of tasks in computer vision and natural language processing. They e…

cs.CL2023

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…

cs.CL2022

Fixing MoE Over-Fitting on Low-Resource Languages in Multilingual Machine Translation

Maha Elbayad, Anna Sun, Shruti Bhosale

Sparsely gated Mixture of Experts (MoE) models have been shown to be a compute-efficient method to scale model capacity for multilingual machine translation. However, for low-resou…

cs.CL2022★ 373 cited

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,…

cs.HC2021

Identifying Terms and Conditions Important to Consumers using Crowdsourcing

Xingyu Liu, Annabel Sun, Jason I. Hong

Terms and conditions (T&Cs) are pervasive on the web and often contain important information for consumers, but are rarely read. Previous research has explored methods to surface a…

cs.CL2021

Playing Codenames with Language Graphs and Word Embeddings

Divya Koyyalagunta, Anna Sun, Rachel Lea Draelos +1

Although board games and video games have been studied for decades in artificial intelligence research, challenging word games remain relatively unexplored. Word games are not as c…