Multi-task attention-based neural networks for implicit discourse relationship representation and identification

Abstract

We present a novel multi-task attention-based neural network model to address implicit discourse relationship representation and identification through two types of representation learning, an attention-based neural network for learning discourse relationship representation with two arguments and a multi-task framework for learning knowledge from annotated and unannotated corpora. The extensive experiments have been performed on two benchmark corpora (i.e., PDTB and CoNLL-2016 datasets). Experimental results show that our proposed model outperforms the state-of-the-art systems on benchmark corpora.