A Multi-stage Strategy for Chinese Discourse Tree Construction

Abstract

Building discourse tree is crucial to improve the performance of discourse parsing. There are two issues in previous work on discourse tree construction, i.e., the error accumulation and the influence of connectives in transition-based algorithms. To address above issues, this paper proposes a tensor-based neural network with the multi-stage strategy and connective deletion mechanism. Experimental results on both CDTB and RST-DT show that our model achieves the state-of-the-art performance.