1 |
The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation ...
|
|
|
|
BASE
|
|
Show details
|
|
3 |
Larger-Scale Transformers for Multilingual Masked Language Modeling ...
|
|
|
|
BASE
|
|
Show details
|
|
4 |
FST: the FAIR Speech Translation System for the IWSLT21 Multilingual Shared Task ...
|
|
|
|
BASE
|
|
Show details
|
|
6 |
Adapting High-resource NMT Models to Translate Low-resource Related Languages without Parallel Data ...
|
|
|
|
BASE
|
|
Show details
|
|
7 |
Multilingual Translation with Extensible Multilingual Pretraining and Finetuning ...
|
|
|
|
BASE
|
|
Show details
|
|
9 |
Multilingual Denoising Pre-training for Neural Machine Translation ...
|
|
|
|
BASE
|
|
Show details
|
|
10 |
Beyond English-Centric Multilingual Machine Translation ...
|
|
Fan, Angela; Bhosale, Shruti; Schwenk, Holger; Ma, Zhiyi; El-Kishky, Ahmed; Goyal, Siddharth; Baines, Mandeep; Celebi, Onur; Wenzek, Guillaume; Chaudhary, Vishrav; Goyal, Naman; Birch, Tom; Liptchinsky, Vitaliy; Edunov, Sergey; Grave, Edouard; Auli, Michael; Joulin, Armand. - : arXiv, 2020
|
|
Abstract:
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. However, much of this work is English-Centric by training only on data which was translated from or to English. While this is supported by large sources of training data, it does not reflect translation needs worldwide. In this work, we create a true Many-to-Many multilingual translation model that can translate directly between any pair of 100 languages. We build and open source a training dataset that covers thousands of language directions with supervised data, created through large-scale mining. Then, we explore how to effectively increase model capacity through a combination of dense scaling and language-specific sparse parameters to create high quality models. Our focus on non-English-Centric models brings gains of more than 10 BLEU when directly translating between non-English directions while performing competitively to the ...
|
|
Keyword:
Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG
|
|
URL: https://dx.doi.org/10.48550/arxiv.2010.11125 https://arxiv.org/abs/2010.11125
|
|
BASE
|
|
Hide details
|
|
11 |
Unsupervised Cross-lingual Representation Learning at Scale ...
|
|
|
|
BASE
|
|
Show details
|
|
12 |
The Social Dynamics of Language Change in Online Networks ...
|
|
|
|
BASE
|
|
Show details
|
|
|
|