Discriminative Reranking of Discourse Parses Using Tree Kernels

In this paper, we present a discrimina- tive approach for reranking discourse trees generated by an existing probabilistic dis- course parser. The reranker relies on tree kernels (TKs) to capture the global depen- dencies between discourse units in a tree. In particular, we design new computa- tional structures of discourse trees, which combined with standard TKs, originate novel discourse TKs. The empirical evalu- ation shows that our reranker can improve the state-of-the-art sentence-level parsing accuracy from 79.77% to 82.15%, a rel- ative error reduction of 11.8%, which in turn pushes the state-of-the-art document- level accuracy from 55.8% to 57.3%.