Using Discourse Structure Improves Machine Translation Evaluation
Francisco Guzmán, Shafiq Joty, Lluı́s Màrquez, Preslav Nakov · 2014
We present experiments in using discourse structure for improving machine translation evaluation.We first design two discourse-aware similarity measures, which use all-subtree kernels to compare discourse parse trees in accordance with the Rhetorical Structure Theory.Then, we show that these measures can help improve a number of existing machine translation evaluation metrics both at the segment-and at the system-level.Rather than proposing a single new metric, we show that discourse information is complementary to the state-of-the-art evaluation metrics, and thus should be taken into account in the development of future richer evaluation metrics.