Implicit discourse relation recognition remains a serious challenge due to the absence of discourse connectives. In this paper, we propose a Shallow Convolutional Neural Network (SCNN) for implicit discourse relation recognition, which contains only one hidden layer but is effective in relation recognition. The shallow structure alleviates the overfitting problem, while the convolution and nonlinear operations help preserve the recognition and generalization ability of our model. Experiments on the benchmark data set show that our model achieves comparable and even better performance when comparing against current state-of-the-art systems.
REF propose a simplified neural network which contains only one hidden layer and use three different pooling operations (max, min, average).
Shallow Convolutional Neural Network for Implicit Discourse Relation Recognition
EMNLP
Computer Science