Discourse Act Classification Using Discussion Patterns with Neural Networks

Abstract

We proposed a model that classifies discussion discourse acts using discussion patterns with neural networks. Several attempts have been made in earlier works to analyze texts that are used in various discussions. The importance of discussion patterns has been explored in those works but their methods required a sophisticated design to combine pattern features with a classifier. Our model introduces tree learning approaches and a graph learning approach to capture discussion patterns without pattern features. In an evaluation to classify discussion discourse acts in Reddit, the model achieved improvements of 1.5% in accuracy and 2.2 in F₁ score compared to the previous best model. We further analyzed the model using an attention mechanism to inspect interactions among different learning approaches.