Predicting Implicit Discourse Relation with Multi-view Modeling and Effective Representation Learning

Abstract

Discourse relations between two text segments play an important role in many natural language processing (NLP) tasks. The connectives strongly indicate the sense of discourse relations, while in fact, there are no connectives in a large proportion of discourse relations, i.e., implicit discourse relations. The key for implicit relation prediction is to correctly model the semantics of the two discourse arguments as well as the contextual interaction between them. To achieve this goal, we propose a multi-view framework that consists of two hierarchies. The first one is the model hierarchy and we propose a neural network based method considering different views. The second one is the feature hierarchy and we learn multi-level distributed representations. We have conducted experiments on the standard benchmark dataset and the results show that compared with several methods our proposed method can achieve the best performance in most cases.