Using Shallow Syntax Information to Improve Word Alignment and Reordering for SMT

We describe two methods to improve SMT accuracy using shallow syntax information. First, we use chunks to refine the set of word alignments typically used as a starting point in SMT systems. Second, we extend an N-gram-based SMT system with chunk tags to better account for long-distance reorderings. Exper-iments are reported on an Arabic-English task showing significant improvements. A human error analysis indicates that long-distance re-orderings are captured effectively.