  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.
