Driving inversion transduction grammar induction with semantic evaluation

Abstract

We describe a new technique for improving statistical machine translation training by adopting scores from a recent crosslingual semantic frame based evaluation metric, XMEANT, as outside probabilities in expectation-maximization based ITG (inversion transduction grammars) alignment. Our new approach strongly biases early-stage SMT learning towards semantically valid alignments. Unlike previous attempts that have proposed using semantic frame based evaluation metrics as the objective function for late-stage tuning of less than a dozen loglinear mixture weights, our approach instead applies the semantic metric at one of the earliest stages of SMT training, where it may impact millions of model parameters. The choice of XMEANT is motivated by empirical studies that have shown ITG constraints to cover almost all crosslingual semantic frame alternations, which resemble the crosslingual semantic frame matching measured by XMEANT. Our experiments purposely restrict training data to small amounts to show the technique's utility in the absence of a huge corpus, to study the effects of semantic generalizations while avoiding overreliance on memorization. Results show that directly driving ITG training with the crosslingual semantic frame based objective function not only helps to further sharpen the ITG constraints, but still avoids excising relevant portions of the search space, and leads to better performance than either conventional ITG or GIZA++ based approaches.