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• W2034991102 abstract "This article describes a method that successfully exploits syntactic features for n-best translation candidate reranking using perceptrons. We motivate the utility of syntax by demonstrating the superior performance of parsers over n-gram language models in differentiating between Statistical Machine Translation output and human translations. Our approach uses discriminative language modelling to rerank the n-best translations generated by a statistical machine translation system. The performance is evaluated for Arabic-to-English translation using NIST’s MT-Eval benchmarks. While deep features extracted from parse trees do not consistently help, we show how features extracted from a shallow Part-of-Speech annotation layer outperform a competitive baseline and a state-of-the-art comparative reranking approach, leading to significant BLEU improvements on three different test sets." @default.
• W2034991102 created "2016-06-24" @default.
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• W2034991102 date "2011-09-01" @default.
• W2034991102 modified "2023-09-24" @default.
• W2034991102 title "Syntactic discriminative language model rerankers for statistical machine translation" @default.
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• W2034991102 doi "https://doi.org/10.1007/s10590-011-9108-7" @default.
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