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

Translation memory (TM) plays an important role in localisation workflow and is used as an efficient and fundamental tool to carry out translation. In recent years, statistical machine translation (SMT) techniques 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 explored some issues of adapting 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 compared the TMX-based SMT systems with a customised SYSTRAN system through human evaluation and automatic evaluation metrics. The experimental results conducted on the French and English language pair show that the SMT can perform well using TMX as input format either during training or at runtime.