@inproceedings{rikters-2016-neural,
    title = "Neural Network Language Models for Candidate Scoring in Hybrid Multi-System Machine Translation",
    author = "Rikters, Mat{\=\i}ss",
    editor = "Lambert, Patrik  and
      Babych, Bogdan  and
      Eberle, Kurt  and
      Banchs, Rafael E.  and
      Rapp, Reinhard  and
      Costa-juss{\`a}, Marta R.",
    booktitle = "Proceedings of the Sixth Workshop on Hybrid Approaches to Translation ({H}y{T}ra6)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://aclanthology.org/W16-4502",
    pages = "8--15",
    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.",
}