In statistical machine translation, decoding without any reordering constraint is an NP-hard problem. Inversion Transduction Grammars (ITGs) exploit linguistic structure and can well balance the needed flexibility against complexity constraints. Currently, translation models with ITG constraints usually employs the cube-time CYK algorithm. In this paper, we present a shift-reduce decoding algorithm that can generate ITG-legal translation from left to right in linear time. This algorithm runs in a reduce-eager style and is suited to phrase-based models. Using the state-ofthe-art decoder Moses as the baseline, experiment results show that the shift-reduce algorithm can significantly improve both the accuracy and the speed on different test sets.
REF proposed a shift-reduce algorithm to add BTG constraints to phrase-based models.
An Efficient Shift-Reduce Decoding Algorithm for Phrased-Based Machine Translation
COLING - POSTERS
Computer Science