A Joint Learning Approach To Explicit Discourse Parsing Via Structured Perceptron

Discourse parsing is a challenging task and plays a critical role in discourse analysis. In this paper, we focus on building an end-to-end PDTB-style explicit discourse parser via structured perceptron by decomposing it into two components, i.e., a connective labeler, which identifies connectives from a text and determines their senses in classifying discourse relationship, and an argument labeler, which identifies corresponding arguments for a given connective. Particularly, to reduce error propagation and incorporate the interaction between the two components, a joint learning approach via structured perceptron is proposed. Evaluation on the PDTB corpus shows that our two-components explicit discourse parser can achieve comparable performance with the state-of-the-art one. It also shows that our joint learning approach can significantly outperform the pipeline ones.