Self-organizing incremental and graph convolution neural network for English implicit discourse relation recognition

Abstract

Implicit discourse relation recognition is a sub-task of discourse relation recognition, which is challenging because it is difficult to learn the argument representation with rich semantic information and interactive information. To solve this problem, this paper proposes a self-organizing incremental and graph convolution neural network for English implicit discourse relation recognition. The method adopts the preliminary training language model BERT (Bidirectional Encoder Representation from Transformers) coding argument for argument. A classification model based on self-organizing incremental and graph convolutional neural network is constructed to obtain the argument representation which is helpful for English implicit discourse relation recognition. The experimental results show that the proposed method is superior to the benchmark model in terms of contingency relations and expansion relations.