A Recurrent Neural Model with Attention for the Recognition of Chinese Implicit Discourse Relations

Samuel Rönnqvist, Niko Schenk, Christian Chiarcos

Abstract: We introduce an attention-based Bi-LSTM for Chinese implicit discourse relations and demonstrate that modeling argument pairs as a joint sequence can outperform word order-agnostic approaches. Our model benefits from a partial sampling scheme and is conceptually simple, yet achieves state-of-the-art performance on the Chinese Discourse Treebank. We also visualize its attention activity to illustrate the model's ability to selectively focus on the relevant parts of an input sequence.