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        <title>Continuous space language models using restricted Boltzmann machines</title>
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            <title>Proceedings of the 9th International Workshop on Spoken Language Translation: Papers</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T Continuous space language models using restricted Boltzmann machines
%A Niehues, Jan
%A Waibel, Alex
%S Proceedings of the 9th International Workshop on Spoken Language Translation: Papers
%D 2012
%8 dec 6 7
%C Hong Kong, Table of contents
%F niehues-2012-continuous
%X 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.
%U https://aclanthology.org/2012.iwslt-papers.3
%P 164-170

Markdown (Informal)

[Continuous space language models using restricted Boltzmann machines](https://aclanthology.org/2012.iwslt-papers.3) (Niehues & Waibel, IWSLT 2012)

• Continuous space language models using restricted Boltzmann machines (Niehues & Waibel, IWSLT 2012)

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.