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- W2786752023 abstract "This paper presents the comparison of how using different neural network based language modeling tools for selecting the best candidate fragments affects the final output translation quality in a hybrid multi-system machine translation setup. Experiments were conducted by comparing perplexity and BLEU scores on common test cases using the same training data set. A 12-gram statistical language model was selected as a baseline to oppose three neural network based models of different characteristics. The models were integrated in a hybrid system that depends on the perplexity score of a sentence fragment to produce the best fitting translations. The results show a correlation between language model perplexity and BLEU scores as well as overall improvements in BLEU." @default.
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- W2786752023 date "2016-12-01" @default.
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- W2786752023 title "Neural Network Language Models for Candidate Scoring in Hybrid Multi-System Machine Translation" @default.
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