Relation classification: CNN or RNN?

Abstract

Convolutional neural networks (CNN) have delivered competitive performance on relation classification, without tedious feature engineering. A particular shortcoming of CNN, however, is that it is less powerful in modeling long-span relations. This paper presents a model based on recurrent neural networks (RNN) and compares the capabilities of CNN and RNN on the relation classification task. We conducted a thorough comparative study on two databases: one is the popular SemEval-2010 Task 8 dataset, and the other is the KBP37 dataset we designed based on MIML-RE [1], with the goal of learning and testing complex relations. The experimental results strongly indicate that even with a simple RNN structure, the model can deliver much better performance than CNN, particularly for long-span relations.