Improving Semantic SMT via Soft Semantic Role Label Constraints on ITG Alignments

We show that applying semantic role label constraints to bracketing ITG alignment to train MT systems improves the quality of MT output in comparison to the conventional BITG and GIZA alignments. Moreover, we show that applying soft constraints to SRL-constrained BITG alignment leads to a better translation system compared to using hard constraints which appear too harsh to produce meaningful biparses. We leverage previous work demonstrating that BITG alignments are able to fully cover cross-lingual semantic frame alternations, by using semantic role labeling to further narrow BITG constraints, in a soft fashion that avoids losing relevant portions of the search space. SRL-based evaluation metrics like MEANT have shown that tuning towards preserving the shallow semantic structure across translations, robustly improves translation performance. Our approach brings the same intuition into the training phase. We show that our new alignment outperforms both conventional Moses and BITG alignment baselines in terms of the adequacy-oriented MEANT scores, while still producing comparable results in terms of edit distance metrics.