Discourse Parsing on Multi-Granularity Interaction

Weihao Liu, Yaxin Fan, Xiaomin Chu, Peifeng Li, Qiaoming Zhu

Abstract: Discourse parsing aims to construct a discourse structure tree to reflect the internal structure of a document. Most existing work only considers parsing documents from the paragraph-level or sentence-level granularity, ignoring the inter-action of different levels of granularity. Therefore, we propose a Multi-Granularity Interaction Method (MGIM) that facilitates discourse parsing through bidirectional information interaction at multi-granularity. We first introduce the structural information at the sentence level to boost paragraph-level parsing, and then use the functional pragmatics information at the paragraph level to guide sentence-level parsing. Moreover, we introduce an auxiliary task, discourse functional pragmatics recognition, to improve sentence-level parsing, which can guide sentence-level discourse tree construction from a macro perspective. Meanwhile, since the research field still lacks data for studying unified Chi-nese discourse parsing, we construct a Unified Chinese Discourse TreeBank UCDTB. Experimental results on both the Chinese UCDTB and the English RST-DT demonstrate the effectiveness of our proposed method.