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- W2238532943 abstract "We present a novel approach to multi-engine machine translation, using a featurebased classification algorithm. Instead of just using language models, translation models, or internal confidence scores, we sought out other features that could be used to determine which of two translations to select. We combined the outputs from a phrase-based system, Moses [Koehn et al., 2007] and a tree-to-tree system [Cowan et al., 2006]. Our main result is a 0.3 to 0.4 improvement in BLEU score over the best single system used, while also improving fluency and adequacy judgements. In addition, we used the same setup to directly predict which sentences would be judged by humans to be more fluent and more adequate. In those domains, we predicted the better sentence 6% to 7% more often than a baseline of always choosing the single best system. Thesis Supervisor: Michael Collins Title: Associate Professor of Computer Science" @default.
- W2238532943 created "2016-06-24" @default.
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- W2238532943 date "2008-01-01" @default.
- W2238532943 modified "2023-09-24" @default.
- W2238532943 title "Combining phrase-based and tree-to-tree translation" @default.
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