Selection-Based Language Model for Domain Adaptation using Topic Modeling

This paper introduces a selection-based LM using topic modeling for the purpose of domain adaptation which is often required in Statistical Machine Translation. The performance of this selection-based LM slightly outperforms the state-of-the-art Moore-Lewis LM by 1.0 % for EN-ES and 0.7 % for ES-EN in terms of BLEU. The performance gain in terms of perplexity was 8 % over the Moore-Lewis LM and 17 % over the plain LM. 1 Domain Adaptation in Statistical Machine Translation Domain adaptation is one important research area in Statistical Machine Translation (SMT) as well as other areas of NLP such as parsing. Domain adaptation tries to ensure that the performance is not radically decreased even if we translate a text in a test set whose genre is different from the parallel corpus which is used to build the system. Without loss of generality, the decoder of an SMT system can be written in the form of the noisy channel model argminE P (E|F)PLM (E) where the two components, the set of P (E|F) and that of PLM (E), are the targets that we do domain adaptation on: the set of P (E|F) is called a phrase table (or a rule table) and that of PLM (E) i