| Abstract | We propose a novel approach for developing a two-stage document-level discourse parser . |
| Abstract | We present two approaches to combine these two stages of discourse parsing effectively. |
| Abstract | A set of empirical evaluations over two different datasets demonstrates that our discourse parser significantly outperforms the state-of-the-art, often by a wide margin. |
| Introduction | Discourse analysis in RST involves two subtasks: discourse segmentation is the task of identifying the EDUs, and discourse parsing is the task of linking the discourse units into a labeled tree. |
| Introduction | While recent advances in automatic discourse segmentation and sentence-level discourse parsing have attained accuracies close to human performance (Fisher and Roark, 2007; J oty et al., 2012), discourse parsing at the document-level still poses significant challenges (Feng and Hirst, 2012) and the performance of the existing document-level parsers (Hemault et al., 2010; Subba and Di-Eugenio, 2009) is still considerably inferior compared to human gold-standard. |
| Introduction | This paper aims to reduce this performance gap and take discourse parsing one step further. |