Neural network language models, or continuous-space language models (CSLMs), have
been shown to improve the performance of statistical machine translation (SMT) when they
are used for reranking n-best translations. However, CSLMs have not been used in the first
pass decoding of SMT, because using CSLMs in decoding takes a lot of time. In contrast, we
propose a method for converting CSLMs into back-off n-gram language models (BNLMs) so
that we can use converted CSLMs in decoding. We show that they outperform the original …

The Language Model (LM) is an essential component of Statistical Machine Translation
(SMT). In this article, we focus on developing efficient methods for LM construction. Our main
contribution is that we propose a Natural N-grams based Converting (NNGC) method for
transforming a Continuous-Space Language Model (CSLM) to a Back-off N-gram Language
Model (BNLM). Furthermore, a Bilingual LM Pruning (BLMP) approach is developed for
enhancing LMs in SMT decoding and speeding up CSLM converting. The proposed pruning …