Dynamically Integrating Cross-Domain Translation Memory into Phrase-Based Machine Translation during Decoding

Our previous work focuses on combining translation memory (TM) and statistical machine translation (SMT) when the TM database and the SMT training set are the same. However, the TM database will deviate from the SMT training set in the real task when time goes by. In this work, we concentrate on the task when the TM database and the SMT training set are different and even from different domains. Firstly, we dynamically merge the matched TM phrase-pairs into the SMT phrase table to meet the real application. Secondly, we propose an improved integrated model to distinguish the original and the new-ly-added phrase-pairs. Thirdly, a simple but effective TM adaptation method is adopted to favor the consistent translations in cross-domain test. Our experiments have shown that merging the TM phrase-pairs achieves significant improvements. Furthermore, the proposed approaches are significantly better than the TM, the SMT and previous integration works for both in-domain and cross-domain tests.