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        <title>Neural Network Language Models for Candidate Scoring in Hybrid Multi-System Machine Translation</title>
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            <title>Proceedings of the Sixth Workshop on Hybrid Approaches to Translation (HyTra6)</title>
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            <namePart type="given">Patrik</namePart>
            <namePart type="family">Lambert</namePart>
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            <namePart type="given">Bogdan</namePart>
            <namePart type="family">Babych</namePart>
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        <name type="personal">
            <namePart type="given">Kurt</namePart>
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        <name type="personal">
            <namePart type="given">Rafael</namePart>
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            <namePart type="given">Marta</namePart>
            <namePart type="given">R</namePart>
            <namePart type="family">Costa-jussà</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Neural Network Language Models for Candidate Scoring in Hybrid Multi-System Machine Translation
%A Rikters, Matīss
%Y Lambert, Patrik
%Y Babych, Bogdan
%Y Eberle, Kurt
%Y Banchs, Rafael E.
%Y Rapp, Reinhard
%Y Costa-jussà, Marta R.
%S Proceedings of the Sixth Workshop on Hybrid Approaches to Translation (HyTra6)
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F rikters-2016-neural
%X 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.
%U https://aclanthology.org/W16-4502
%P 8-15

Markdown (Informal)

[Neural Network Language Models for Candidate Scoring in Hybrid Multi-System Machine Translation](https://aclanthology.org/W16-4502) (Rikters, HyTra 2016)

• Neural Network Language Models for Candidate Scoring in Hybrid Multi-System Machine Translation (Rikters, HyTra 2016)

ACL

• Matīss Rikters. 2016. Neural Network Language Models for Candidate Scoring in Hybrid Multi-System Machine Translation. In Proceedings of the Sixth Workshop on Hybrid Approaches to Translation (HyTra6), pages 8–15, Osaka, Japan. The COLING 2016 Organizing Committee.