PanDoRA: a large-scale two-way statistical machine translation system for hand-held devices
Ying Zhang, Stephan Vogel · 2007
The statistical machine translation (SMT) approach has taken a lead place in the field of Machine Translation for its better translation quality and lower cost in training compared to other approaches. However, due to the high demand of computing resources, an SMT system can not be directly run on hand-held devices. Most existing hand-held translation systems are either interlingua-based, which require non-trivial human efforts to write grammar rules, or using the client/server architecture, which are constrained by the availability of wireless connections. In this paper we present PanDoRA, a two-way phrase-based statistical machine translation system for stand-alone hand-held devices. Powered by special designs such as integerized computation and compact data structure, PanDoRA can translate dialogue speech on off-the-shelf PDAs in real time. PanDoRA uses 64K words vocabulary and millions of phrase pairs for each translation directions. To our knowledge, PanDoRA is the first large-scale SMT system with build-in reordering models running on hand-held devices. We have successfully developed several speech-to-speech translation systems using PanDoRA and our experiments show that PanDoRA's translation quality is comparable to that of the state-of-the-art phrase-based statistical machine translation systems such as Pharaoh and STTK.