Length Adaptive Recurrent Model for Text Classification
Zhengjie Huang, Zi Ye, Shuangyin Li, Rong Pan · 2017
In recent years, recurrent neural networks have been widely used for various text classification tasks. However, most of the recurrent architectures will not assign a class label to a text until they read the last word, while human beings are able to determine the text class before reading the whole text. In this paper, we propose a Length Adaptive Recurrent Model (LARM) which can automatically determine the minimum text length that is necessary to perform the classification. With three parts includingReader, Predictor andAgent, our model is designed to read a text word by word, and terminate the process when the adequate information has been caught for the text classification task. The experimental results show that our model has comparable or even better performance compared to the vanilla LSTM when both are fed with partial text input. Besides, we can speed up text classification by truncating the text when sufficient evidence is found for classification. Furthermore, we also visualize our model and show that our model works like human beings, who can gradually come up with the general idea of a text while reading texts sequentially.