Neural Network Language Models for Candidate Scoring in Hybrid Multi-System Machine Translation

Matīss Rikters


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.
Anthology ID:
W16-4502
Volume:
Proceedings of the Sixth Workshop on Hybrid Approaches to Translation (HyTra6)
Month:
December
Year:
2016
Address:
Osaka, Japan
Venues:
HyTra | WS
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
8–15
Language:
URL:
https://www.aclweb.org/anthology/W16-4502
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/W16-4502.pdf