Multi-Timescale Long Short-Term Memory Neural Network for Modelling Sentences and Documents

Pengfei Liu, Xipeng Qiu, Xinchi Chen, Shiyu Wu, Xuanjing Huang

Abstract: Neural network based methods have obtained great progress on a variety of natural language processing tasks. However, it is still a challenge task to model long texts, such as sentences and documents. In this paper, we propose a multi-timescale long short-termmemory (MT-LSTM) neural network to model long texts. MTLSTM partitions the hidden states of the standard LSTM into several groups. Each group is activated at different time periods. Thus, MT-LSTM can model very long documents as well as short sentences. Experiments on four benchmark datasets show that our model outperforms the other neural models in text classification task.