Intelligent English Tense Collocation and Evaluation Based on Deep Reinforcement Learning

Abstract

The representation of time in sentences is the key problem for tense collocation. Based on the relationship expression among regions in Allen's interval algebra theory, we propose a vector representation method, i.e., relationship vector, and several operations are defined based on the relationship vector for temporal reasoning in this work. This method transforms the original matrix representation into vector representation, which reduces the amount of computation of temporal reasoning. In addition, we propose a temporal classification and collocation method based on deep learning and deep reinforcement learning. This method uses a bidirectional cyclic neural network and a convolutional neural network for text expression and achieves temporal word classification and temporal collocation based on the deep reinforcement learning model. In the experiments, the proposed method obtains the average accuracy of 92.17% in five datasets, i.e., MPQA, CR, MR, Subj, and TREC, which proves its effectiveness in tense collocation.