TMX Markup: A Challenge When Adapting SMT to the Localisation Environment

Translation memory (TM) plays an impor-tant role in localisation workflows and is used as an efficient and fundamental tool to carry out translation. In recent years, statistical machine translation (SMT) tech-niques have been rapidly developed, and the translation quality and speed have been significantly improved as well. However, when applying SMT technique to facilitate post-editing in the localisation industry, we need to adapt SMT to the TM data which is formatted with special mark-up. In this paper, we explore some issues when adapt-ing SMT to Symantec formatted TM data. Three different methods are proposed to handle the Translation Memory eXchange (TMX) markup and a comparative study is carried out between them. Furthermore, we also compare the TMX-based SMT systems with a customised SYSTRAN sys-tem through human evaluation and auto-matic evaluation metrics. The experimen-tal results conducted on the French and En-glish language pair show that the SMT can perform well using TMX as input format either during training or at runtime.