Use of Domain Specific Language Resources in Machine Translation
In this paper, we address the problem of Machine Translation (MT) for a specialised domain in a language pair for which only a very small domain specific parallel corpus is available. We conduct a series of experiments using a purely phrase based SMT (PBSMT) system and a hybrid MT system (TectoMT) testing three different strategies to overcome the problem of the small amount of in domain training data. Our results show that adding a small size in domain bilingual terminology to the small in domain training corpus leads to the best improvements of a hybrid MT system, while the PBSMT system achieves the best results by adding a combination of in domain bilingual terminology and a larger out of domain corpus. We focus on qualitative human evaluation of the output of two best systems (one for each approach) and perform a systematic in depth error analysis which revealed advantages of the hybrid MT system over the pure PBSMT system for this specific task.
LREC