Discriminative Reranking of Discourse Parses Using Tree Kernels
Shafiq Joty, Alessandro Moschitti · 2014
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%. 1