activity
20192026
most citedBeyond English-Centric Multilingual Machine Translation

470 citations · 1.2k across the 10 of their papers we have counts for

collaborators

13 papers

cs.CL2026

TLPO: Token-Level Policy Optimization for Mitigating Language Confusion in Large Language Models

Jinho Choo, JunSeung Lee, Jimyeong Kim +3

Large language models (LLMs) demonstrate strong multilingual capabilities, yet often fail to consistently generate responses in the intended language, exhibiting a phenomenon known…

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★ 41 cited

Seamless: Multilingual Expressive and Streaming Speech Translation

Seamless Communication, Loïc Barrault, Yu-An Chung +62

Large-scale automatic speech translation systems today lack key features that help machine-mediated communication feel seamless when compared to human-to-human dialogue. In this wo…

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.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.CL2022★ 6 cited

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…

cs.CL2021★ 82 cited

The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation

Naman Goyal, Cynthia Gao, Vishrav Chaudhary +7

One of the biggest challenges hindering progress in low-resource and multilingual machine translation is the lack of good evaluation benchmarks. Current evaluation benchmarks eithe…

cs.CL2020

Generating Fact Checking Briefs

Angela Fan, Aleksandra Piktus, Fabio Petroni +5

Fact checking at scale is difficult -- while the number of active fact checking websites is growing, it remains too small for the needs of the contemporary media ecosystem. However…

cs.CL2020★ 470 cited

Beyond English-Centric Multilingual Machine Translation

Angela Fan, Shruti Bhosale, Holger Schwenk +14

Existing work in translation demonstrated the potential of massively multilingual machine translation by training a single model able to translate between any pair of languages. Ho…

cs.CL2019★ 246 cited

CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data

Guillaume Wenzek, Marie-Anne Lachaux, Alexis Conneau +4

Pre-training text representations have led to significant improvements in many areas of natural language processing. The quality of these models benefits greatly from the size of t…

cs.CL2019

CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEB

Holger Schwenk, Guillaume Wenzek, Sergey Edunov +2

We show that margin-based bitext mining in a multilingual sentence space can be applied to monolingual corpora of billions of sentences. We are using ten snapshots of a curated com…

cs.CL2019

Unsupervised Cross-lingual Representation Learning at Scale

Alexis Conneau, Kartikay Khandelwal, Naman Goyal +7

This paper shows that pretraining multilingual language models at scale leads to significant performance gains for a wide range of cross-lingual transfer tasks. We train a Transfor…

cs.CL2019★ 3 cited

Facebook AI's WAT19 Myanmar-English Translation Task Submission

Peng-Jen Chen, Jiajun Shen, Matt Le +5

This paper describes Facebook AI's submission to the WAT 2019 Myanmar-English translation task. Our baseline systems are BPE-based transformer models. We explore methods to leverag…