Paper: Neural Network Based Bilingual Language Model Growing for Statistical Machine Translation

ACL ID D14-1023
Title Neural Network Based Bilingual Language Model Growing for Statistical Machine Translation
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2014
Authors

Since larger n-gram Language Model (LM) usually performs better in Statistical Machine Translation (SMT), how to con- struct efficient large LM is an important topic in SMT. However, most of the ex- isting LM growing methods need an extra monolingual corpus, where additional LM adaption technology is necessary. In this paper, we propose a novel neural network based bilingual LM growing method, only using the bilingual parallel corpus in SMT. The results show that our method can im- prove both the perplexity score for LM e- valuation and BLEU score for SMT, and significantly outperforms the existing LM growing methods without extra corpus.