Memory Augmented Attention Model for Chinese Implicit Discourse Relation Recognition

Recently, Chinese implicit discourse relation recognition has attracted more and more attention, since it is crucial to understand the Chinese discourse text. In this paper, we propose a novel memory augmented attention model which represents the arguments using an attention-based neural network and preserves the crucial information with an external memory network which captures each discourse relation clustering structure to support the relation inference. Extensive experiments demonstrate that our proposed model can achieve the new stateof-the-art results on Chinese Discourse Treebank. We further leverage network visualization to show why our attention and memory model are effective.