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