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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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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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Abstract:
We present an approach based on multilingual sentence embeddings to automatically extract parallel sentences from the content of Wikipedia articles in 85 languages, including several dialects or low-resource languages. We do not limit the the extraction process to alignments with English, but systematically consider all possible language pairs. In total, we are able to extract 135M parallel sentences for 1620 different language pairs, out of which only 34M are aligned with English. This corpus of parallel sentences is freely available at https://github.com/facebookresearch/LASER/tree/master/tasks/WikiMatrix. To get an indication on the quality of the extracted bitexts, we train neural MT baseline systems on the mined data only for 1886 languages pairs, and evaluate them on the TED corpus, achieving strong BLEU scores for many language pairs. The WikiMatrix bitexts seem to be particularly interesting to train MT systems between distant languages without the need to pivot through English. ... : 13 pages, 3 figures, 6 tables ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://dx.doi.org/10.48550/arxiv.1907.05791 https://arxiv.org/abs/1907.05791
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