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• W2891089320 abstract "Measuring domain relevance of data and identifying or selecting well-fit domain data for machine translation (MT) is a well-studied topic, but denoising is not yet. Denoising is concerned with a different type of data quality and tries to reduce the negative impact of data noise on MT training, in particular, neural MT (NMT) training. This paper generalizes methods for measuring and selecting data for domain MT and applies them to denoising NMT training. The proposed approach uses trusted data and a denoising curriculum realized by online data selection. Intrinsic and extrinsic evaluations of the approach show its significant effectiveness for NMT to train on data with severe noise." @default.
• W2891089320 created "2018-09-27" @default.
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• W2891089320 date "2018-08-31" @default.
• W2891089320 modified "2023-09-28" @default.
• W2891089320 title "Denoising Neural Machine Translation Training with Trusted Data and Online Data Selection" @default.
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• W2891089320 doi "https://doi.org/10.48550/arxiv.1809.00068" @default.
• W2891089320 hasPublicationYear "2018" @default.
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