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The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation ...
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LAWDR: Language-Agnostic Weighted Document Representations from Pre-trained Models ...
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Classification-based Quality Estimation: Small and Efficient Models for Real-world Applications ...
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Findings of the AmericasNLP 2021 Shared Task on Open Machine Translation for Indigenous Languages of the Americas ...
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Alternative Input Signals Ease Transfer in Multilingual Machine Translation ...
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Adapting High-resource NMT Models to Translate Low-resource Related Languages without Parallel Data ...
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Findings of the WMT 2021 Shared Task on Quality Estimation ...
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AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages ...
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Findings of the WMT 2021 shared task on quality estimation
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In: 689 ; 730 (2021)
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Multilingual Translation with Extensible Multilingual Pretraining and Finetuning ...
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MLQE-PE: A Multilingual Quality Estimation and Post-Editing Dataset ...
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Beyond English-Centric Multilingual Machine Translation ...
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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
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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 ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG
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URL: https://dx.doi.org/10.48550/arxiv.2010.11125 https://arxiv.org/abs/2010.11125
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Unsupervised quality estimation for neural machine translation
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In: 8 ; 539 ; 555 (2020)
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An exploratory study on multilingual quality estimation
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In: 366 ; 377 (2020)
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BERGAMOT-LATTE submissions for the WMT20 quality estimation shared task
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In: 1010 ; 1017 (2020)
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Findings of the WMT 2020 shared task on quality estimation
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In: 743 ; 764 (2020)
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MLQE-PE: A multilingual quality estimation and post-editing dataset
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Unsupervised Cross-lingual Representation Learning at Scale ...
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WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia ...
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