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• W2250612533 abstract "Lexical selection is of great importance to statistical machine translation. In this paper, we propose a graph-based framework for collective lexical selection. The framework is established on a translation graph that captures not only local associations between source-side content words and their target translations but also targetside global dependencies in terms of relatedness among target items. We also introduce a random walk style algorithm to collectively identify translations of sourceside content words that are strongly related in translation graph. We validate the effectiveness of our lexical selection framework on Chinese-English translation. Experiment results with large-scale training data show that our approach significantly improves lexical selection." @default.
• W2250612533 created "2016-06-24" @default.
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• W2250612533 date "2015-01-01" @default.
• W2250612533 modified "2023-10-18" @default.
• W2250612533 title "Graph-Based Collective Lexical Selection for Statistical Machine Translation" @default.
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• W2250612533 doi "https://doi.org/10.18653/v1/d15-1145" @default.
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