An Efficient Two-Pass Approach to Synchronous-CFG Driven Statistical MT

We present an efficient, novel two-pass approach to mitigate the computational impact resulting from online intersection of an n-gram language model (LM) and a probabilistic synchronous context-free grammar (PSCFG) for statistical machine translation. In first pass CYK-style decoding, we consider first-best chart item approximations, generating a hypergraph of sentence spanning target language derivations. In the second stage, we instantiate specific alternative derivations from this hypergraph, using the LM to drive this search process, recovering from search errors made in the first pass. Model search errors in our approach are comparable to those made by the state-of-the-art “Cube Pruning” approach in (Chiang, 2007) under comparable pruning conditions evaluated on both hierarchical and syntax-based grammars.