Multi-level Cohesion Information Modeling for Better Written and Dialogue Discourse Parsing

Abstract

Discourse parsing has attracted more and more attention due to its importance on Natural Language Understanding. Accordingly, various neural models proposed and have achieved certain success. However, due to the scale limitation of corpus, outstanding performance still depends on additional features. Different from previous neural studies employing simple flat word level EDU (Elementary Discourse Unit) representation, we improve the performance of discourse parsing by employing cohesion information (In this paper, we regard lexical chain and coreference chain as cohesion information) enhanced EDU representation. In particular, firstly we use WordNet and a coreference resolution model to extract lexical and coreference chain respectively and automatically. Secondly, we construct EDU level graph based on the extracted chains. Finally, using Graph Attention Network, we incorporate the obtained cohesion information into EDU representation to improve discourse parsing. Experiments on RST-DT, CDTB and STAC show our proposed cohesion information enhanced EDU representation can benefit both written and dialogue discourse parsing, compared with the baseline model we duplicated.