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Identifying implicit discourse relations between text spans is a challengingtask because it requires understanding the meaning of the text. To tackle thistask, recent studies have tried several deep learning methods but few of themexploited the syntactic information. In this work, we explore the idea ofincorporating syntactic parse tree into neural networks. Specifically, weemploy the Tree-LSTM model and Tree-GRU model, which are based on the treestructure, to encode the arguments in a relation. Moreover, we further leveragethe constituent tags to control the semantic composition process in thesetree-structured neural networks. Experimental results show that our methodachieves state-of-the-art performance on PDTB corpus.

Tag-Enhanced Tree-Structured Neural Networks for Implicit Discourse Relation Classification