FOLSOM: a fast and memory-efficient phrase-based approach to statistical machine translation

In this work, we propose a novel framework for performing phrase-based statistical machine translation using weighted finite-state transducers (WFST's) that is significantly faster than existing frameworks while also being memory-efficient. In particular, we represent the entire translation model with a single WFST that is statically optimized, in contrast to previous work that represents the translation model as multiple WFST's that must be composed on the fly. We describe a new search algorithm that conveniently and efficiently combines multiple knowledge sources during decoding. The proposed approach is particularly suitable for converged real-time speech translation on scalable computing devices. We were able to develop a SMT system that can translate more than 3000 words/second while still retaining excellent accuracy.