A unified framework and models for integrating translation memory into phrase-based statistical machine translation. (March 2019) - Liu, Yang; Wang, Kun; Zong, Chengqing; Su, Keh-Yih
Highlights: A unified framework and four models which integrate translation memory into PBMT for different scenarios. We propose a unified framework for integrating translation memory into phrase-based statistical machine translation. We implement a model (based on the unified framework) for adopting the TM database as the SMT training set. We implement a model (based on the unified framework) for adopting a different SMT training set from the same domain. We implement two models (based on the unified framework) for adopting a different cross-domain SMT training set scenario. Abstract: Since statistical machine translation (SMT) and translation memory (TM) complement each other in TM matched and unmatched regions, a unified framework for integrating TM into phrase-based SMT is proposed in this paper. Unlike previous two-stage pipeline approaches, which directly merge TM results into the input sentences and subsequently let the SMT only translates those unmatched regions, the proposed framework refers to the corresponding TM information associated with each phrase at the SMT decoding. Under this unified framework, several integrated models are proposed to incorporate different types of information extracted from TM to guide the SMT decoding. We thus let SMT implicitly and indirectly utilize global context with a local dependency model. Furthermore, the SMT phrase table is dynamically enhanced with TM phrase pairs when the TM database and the SMT training set are different. On a Chinese–English TM database, our experiments show that the proposed Model-I significantly improves over both SMT and TM when the SMT training set is also adopted as the TM database and when the fuzzy match score is over 0.4 (overall 3.5 BLEU points improvement and 2.6 TER points reduction). In addition, the proposed Model-II is significantly better than the TM and the SMT systems when the SMT training set and the TM database are different. Furthermore, the proposed Model-III outperforms both the TM and the SMT systems even when the SMT training set and the TM database are from different domains. Additionally, the proposed Model-IV further achieves significant improvements with the help of Top-N TM sentence pairs. Lastly, all our models significantly outperform those state-of-the-art approaches under all test conditions.