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• W2250872883 abstract "Using machine translation output as a starting point for human translation has become an increasingly common application of MT. We propose and evaluate three computationally efficient online methods for updating statistical MT systems in a scenario where post-edited MT output is constantly being returned to the system: (1) adding new rules to the translation model from the post-edited content, (2) updating a Bayesian language model of the target language that is used by the MT system, and (3) updating the MT system’s discriminative parameters with a MIRA step. Individually, these techniques can substantially improve MT quality, even over strong baselines. Moreover, we see super-additive improvements when all three techniques are used in tandem." @default.
• W2250872883 created "2016-06-24" @default.
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• W2250872883 date "2014-01-01" @default.
• W2250872883 modified "2023-10-16" @default.
• W2250872883 title "Learning from Post-Editing: Online Model Adaptation for Statistical Machine Translation" @default.
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• W2250872883 doi "https://doi.org/10.3115/v1/e14-1042" @default.
• W2250872883 hasPublicationYear "2014" @default.
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