Continuous space language models using restricted Boltzmann machines

Jan Niehues, Alex Waibel


Abstract
We present a novel approach for continuous space language models in statistical machine translation by using Restricted Boltzmann Machines (RBMs). The probability of an n-gram is calculated by the free energy of the RBM instead of a feedforward neural net. Therefore, the calculation is much faster and can be integrated into the translation process instead of using the language model only in a re-ranking step. Furthermore, it is straightforward to introduce additional word factors into the language model. We observed a faster convergence in training if we include automatically generated word classes as an additional word factor. We evaluated the RBM-based language model on the German to English and English to French translation task of TED lectures. Instead of replacing the conventional n-gram-based language model, we trained the RBM-based language model on the more important but smaller in-domain data and combined them in a log-linear way. With this approach we could show improvements of about half a BLEU point on the translation task.
Anthology ID:
2012.iwslt-papers.3
Volume:
Proceedings of the 9th International Workshop on Spoken Language Translation: Papers
Month:
December 6-7
Year:
2012
Address:
Hong Kong, Table of contents
Venue:
IWSLT
SIG:
SIGSLT
Publisher:
Note:
Pages:
164–170
Language:
URL:
https://aclanthology.org/2012.iwslt-papers.3
DOI:
Bibkey:
Cite (ACL):
Jan Niehues and Alex Waibel. 2012. Continuous space language models using restricted Boltzmann machines. In Proceedings of the 9th International Workshop on Spoken Language Translation: Papers, pages 164–170, Hong Kong, Table of contents.
Cite (Informal):
Continuous space language models using restricted Boltzmann machines (Niehues & Waibel, IWSLT 2012)
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PDF:
https://aclanthology.org/2012.iwslt-papers.3.pdf