Dynamic Phrase Tables for Machine Translation in an Interactive Post-editing Scenario

This paper presents a phrase table implementation for theMoses system that computes phrase table entries for phrase-based statistical machine translation (PBSMT) on demand by sampling an indexed bitext. While this approach has been used for years in hierarchical phrase-based translation, the PBSMT community has been slow to adopt this paradigm, due to concerns that this would be slow and lead to lower translation quality. The experiments conducted in the course of this work provide evidence to the contrary: without loss in translation quality, the sampling phrase table ranks second out of four in terms of speed, being slightly slower than hash table look-up (Junczys-Dowmunt, 2012) and considerably faster than current implementations of the approach suggested by Zens and Ney (2007). In addition, the underlying parallel corpus can be updated in real time, so that professionally produced translations can be used to improve the quality of the machine translation engine immediately.