@InProceedings{rikters:2016:HyTra6,
  author    = {Rikters, Mat\={\i}ss},
  title     = {Neural Network Language Models for Candidate Scoring in Hybrid Multi-System Machine Translation},
  booktitle = {Proceedings of the Sixth Workshop on Hybrid Approaches to Translation (HyTra6)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  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.},
  url       = {http://aclweb.org/anthology/W16-4502}
}