Integrating a Rule-based with a Hierarchical Translation System

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.

• Type: Conference
• Year: 2010
• Where: LREC
• Authors: Yu Chen, Andreas Eisele
• Keywords: Education, LREC 2010, Machine Translation, RBMT Systems, Rule-Based Machine Translation