Toward High Performance Machine Translation: Preliminary Results from Massively Parallel Memory-Based Translation on SNAP*
Hiroaki Kitano, DAN I. MOLDOVAN, Seungho Cha · 1991
This paper describes a memory-based machine translation system developed for the Semantic Net-work Array Processor (SNAP). The goal of our work is to develop a scalable and high-performance memory-based machine translation system which utilizes the high degree of parallelism provided by the SNAP machine. We have implemented an ex-perimental machine translation system DMSNAP as a central part of a real-time speech-to-speech dia-logue translation system. It is a SNAP version of the ΦDMDIALOG speech-to-speech translation sys-tem. Memory-based natural language processing and syntactic constraint network model has been incorporated using parallel marker-passing which is directly supported from hardware level. Exper-imental results demonstrate that the parsing of a sentence is done in the order of milliseconds. 1