A semantically confidence-weighted ITG induction algorithm

We propose a new algorithm to induce inversion transduction grammars, in which a crosslingual semantic frame based objective function is injected as confidence weighting in the early stages of statistical machine translation training. Unlike recent work on improving translation adequacy that uses a monolingual semantic frame based objective function to drive the tuning of loglinear mixture weights in the late stages of statistical machine translation training, our bilingual approach incorporates the semantic objective during the actual learning of the translation model’s structure. Our approach assigns higher confidence to training examples in which the semantic frames in the input language more closely match the semantic frames of the output language, as predicted automatically by XMEANT, the crosslingual semantic frame based machine translation evaluation metric. We chose to apply this approach to induce inversion transduction grammars (ITGs), since ITG alignments prune a large majority of the space of possible alignments, while at the same time empirically fully covering all the crosslingual semantic frame alternations of the type we are using for confidence weighting. Results show that boosting semantically compatible training examples in ITG induction improves Copyright c © by the paper’s authors. Copying permitted for private and academic purposes. In Proceedings of 3rd International Workshop on Semantic Machine Learning (SML 2016), 10th July 2016, New York City, NY, USA. the translation performance compared to either traditional GIZA++ alignment or conventional ITG alignment based approaches for phrase based statistical machine translation.