Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Field |
|---|---|---|
2016 | arXiv: Computation and Language | Computer science,Pooling,Recurrent neural network,Natural language processing,Artificial intelligence,Sentence,Machine learning,Empirical research |
DocType | Volume | Citations |
Journal | abs/1609.09171 | 0 |
PageRank | References | Authors |
0.34 | 15 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lei Shen | 1 | 0 | 3.38 |
| Junlin Zhang | 2 | 21 | 2.45 |