Integrating a Rule-based with a Hierarchical Translation System
Yu Chen, Andreas Eisele · 2010
Recent developments on hybrid systems that combine rule-based machine translation (RBMT) systems with statistical machine translation (SMT) generally neglect the fact that RBMT systems tend to produce more syntactically well-formed translations than data-driven systems.This paper proposes a method that alleviates this issue by preserving more useful structures produced by RBMT systems and utilizing them in a SMT system that operates on hierarchical structures instead of flat phrases alone.For our experiments, we use Joshua as the decoder (Li et al., 2009).It is the first attempt towards a tighter integration of MT systems from different paradigms that both support hierarchical analyses.Preliminary results show consistent improvements over the previous approach.