Macro Discourse Relation Recogniztion Based on Micro Discourse Structure and Self-Interactive Attention Network

Yaxin Fan, Feng Jiang, Peifeng Li, Qiaoming Zhu

Abstract: Macro discourse relation recognition is an important task of macro discourse analysis. The existing models ignore the micro discourse structure within paragraphs and could not accurately grasp the paragraph semantics. In addition, the traditional pre-trained models only used the representation of the [CLS] token for macro relation classification, lacking more detailed semantic interaction between discourse units. To solve the above issues, we proposed a macro discourse relation recognition model based on Micro Discourse Structure and Self-Interactive Network (MDSSIN) that mines the semantic representation of important parts within a paragraph and enhances semantic interaction between paragraphs. Specially, we first automatically build the micro-structure discourse tree for each paragraph and get the core clause in each paragraph according to nuclearity. Then, we use the self-interactive attention mechanism to capture more detailed semantic interaction between discourse units and between the core clauses of discourse units. Experimental results on Chinese MCDTB show that our method achieves the SOTA performance.