This work builds on our initial study of semantic divergences (Carpuat et al., 2017), where we provide a framework for evaluating the impact of meaning mismatches in parallel segments on MT via data selection: we show that filtering out the most divergent segments in a training corpus improves translation quality.
we previously detect mismatches using a cross-lingual entailment classifier, which is based on surface features only, and requires manually annotated training examples (Negri et al., 2012.