Empirical Evaluation of RNN Architectures on Sentence Classification Task

Recurrent Neural Networks have achieved state-of-the-art results for many problems in NLP and two most popular RNN architectures are “Tail Model” and “Pooling Model”. In this paper, a hybrid architecture is proposed and we present the first empirical study using LSTMs to compare performance of the three RNN structures on sentence classification task. Experimental results show that the “Max Pooling Model” or “Hybrid Max Pooling Model” achieves the best performance on most datasets, while “Tail Model” does not outperform other models.