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The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation ...
Abstract: One of the biggest challenges hindering progress in low-resource and multilingual machine translation is the lack of good evaluation benchmarks. Current evaluation benchmarks either lack good coverage of low-resource languages, consider only restricted domains, or are low quality because they are constructed using semi-automatic procedures. In this work, we introduce the FLORES-101 evaluation benchmark, consisting of 3001 sentences extracted from English Wikipedia and covering a variety of different topics and domains. These sentences have been translated in 101 languages by professional translators through a carefully controlled process. The resulting dataset enables better assessment of model quality on the long tail of low-resource languages, including the evaluation of many-to-many multilingual translation systems, as all translations are multilingually aligned. By publicly releasing such a high-quality and high-coverage dataset, we hope to foster progress in the machine translation community and beyond. ...
Keyword: Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences
URL: https://dx.doi.org/10.48550/arxiv.2106.03193
https://arxiv.org/abs/2106.03193
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2
LAWDR: Language-Agnostic Weighted Document Representations from Pre-trained Models ...
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3
Classification-based Quality Estimation: Small and Efficient Models for Real-world Applications ...
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4
Improving Zero-Shot Translation by Disentangling Positional Information ...
Liu, Danni; Niehues, Jan; Cross, James. - : Association for Computational Linguistics, 2021
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5
As Easy as 1, 2, 3: Behavioural Testing of NMT Systems for Numerical Translation ...
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6
Putting words into the system's mouth: A targeted attack on neural machine translation using monolingual data poisoning ...
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7
Detecting Hallucinated Content in Conditional Neural Sequence Generation ...
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8
Alternative Input Signals Ease Transfer in Multilingual Machine Translation ...
Sun, Simeng; Fan, Angela; Cross, James. - : arXiv, 2021
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9
Improving Zero-Shot Translation by Disentangling Positional Information ...
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10
XLEnt: Mining a Large Cross-lingual Entity Dataset with Lexical-Semantic-Phonetic Word Alignment ...
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11
Adapting High-resource NMT Models to Translate Low-resource Related Languages without Parallel Data ...
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12
Improving Zero-Shot Translation by Disentangling Positional Information
Liu, Danni; Li, Xian; Niehues, Jan. - : Association for Computational Linguistics, 2021
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13
Massively Multilingual Document Alignment with Cross-lingual Sentence-Mover's Distance ...
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14
MLQE-PE: A Multilingual Quality Estimation and Post-Editing Dataset ...
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15
Improving Zero-Shot Translation by Disentangling Positional Information ...
Liu, Danni; Niehues, Jan; Cross, James. - : arXiv, 2020
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16
Unsupervised quality estimation for neural machine translation
In: 8 ; 539 ; 555 (2020)
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17
An exploratory study on multilingual quality estimation
In: 366 ; 377 (2020)
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18
BERGAMOT-LATTE submissions for the WMT20 quality estimation shared task
In: 1010 ; 1017 (2020)
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19
Findings of the WMT 2020 shared task on quality estimation
In: 743 ; 764 (2020)
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20
MLQE-PE: A multilingual quality estimation and post-editing dataset
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