Paper: Using Discourse Structure Improves Machine Translation Evaluation

ACL ID P14-1065
Title Using Discourse Structure Improves Machine Translation Evaluation
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

We present experiments in using dis- course 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 com- plementary to the state-of-the-art evalu- ation metrics, and thus should be taken into account in the development of future richer evaluation metrics.