SECNN: Squeeze-and-Excitation Convolutional Neural Network for Sentence Classification
This work addresses sentence classification in NLP, but it appears incremental as it adapts an existing attention mechanism to CNNs.
The paper tackles the problem of sentence classification by addressing CNN's limitation in capturing long-term dependencies, proposing SECNN which uses a channel attention mechanism on CNN feature maps, and reports achieving advanced performance.
Sentence classification is one of the basic tasks of natural language processing. Convolution neural network (CNN) has the ability to extract n-grams features through convolutional filters and capture local correlations between consecutive words in parallel, so CNN is a popular neural network architecture to dealing with the task. But restricted by the width of convolutional filters, it is difficult for CNN to capture long term contextual dependencies. Attention is a mechanism that considers global information and pays more attention to keywords in sentences, thus attention mechanism is cooperated with CNN network to improve performance in sentence classification task. In our work, we don't focus on keyword in a sentence, but on which CNN's output feature map is more important. We propose a Squeeze-and-Excitation Convolutional neural Network (SECNN) for sentence classification. SECNN takes the feature maps from multiple CNN as different channels of sentence representation, and then, we can utilize channel attention mechanism, that is SE attention mechanism, to enable the model to learn the attention weights of different channel features. The results show that our model achieves advanced performance in the sentence classification task.