Integrating Tree Structures and Graph Structures with Neural Networks to Classify Discussion Discourse Acts

We proposed a model that integrates discussion structures with neural networks to classify discourse acts. Several attempts have been made in earlier works to analyze texts that are used in various discussions. The importance of discussion structures has been explored in those works but their methods required a sophisticated design to combine structural features with a classifier. Our model introduces tree learning approaches and a graph learning approach to directly capture discussion structures without structural features. In an evaluation to classify discussion discourse acts in Reddit, the model achieved improvements of 1.5 in accuracy and 2.2 in FB1 score compared to the previous best model. We further analyzed the model using an attention mechanism to inspect interactions among different learning approaches.