A Stacking Gated Neural Architecture for Implicit Discourse Relation Classification

摘要

Discourse parsing is considered as one of the most challenging natural language processing (NLP) tasks. Implicit discourse relation clas-sification is the bottleneck for discourse parsing. Without the guide of explicit discourse connectives, the relation of sentence pairs are very hard to be inferred. This paper proposes a stacking neural network model to solve the classification problem in which a convolutional neural network (CNN) is utilized for sentence modeling and a collaborative gated neural network (CGNN) is proposed for feature transformation. Our evaluation and comparisons show that the proposed model outperforms previous state-of-the-art systems.