Semantically driven inversion transduction grammar induction for early stage training of spoken language translation

Meriem Beloucif, Dekai Wu

Abstract: We propose an approach in which we inject a crosslingual semantic frame based objective function directly into inversion transduction grammar (ITG) induction in order to semantically train spoken language translation systems. This approach represents a follow-up of our recent work on improving machine translation quality by tuning loglinear mixture weights using a semantic frame based objective function in the late, final stage of statistical machine translation training. In contrast, our new approach injects a semantic frame based objective function back into earlier stages of the training pipeline, during the actual learning of the translation model, biasing learning toward semantically more accurate alignments. Our work is motivated by the fact that ITG alignments have empirically been shown to fully cover crosslingual semantic frame alternations. We show that injecting a crosslingual semantic based objective function for driving ITG induction further sharpens the ITG constraints, leading to better performance than either the conventional ITG or the traditional GIZA++ based approaches.