Tree LSTM Guided Attention Pooling of DCNN for Semantic Sentence Modeling
The ability to explicitly represent sentences is central to natural language processing. Convolutional neural network (CNN) recurrent neural network and recursive neural networks are mainstream architectures. We introduce a novel structure to combine the strength of them for semantic modelling of sentences. Sentence representations are generated by Dynamic CNN (DCNN, a variant of CNN) At pooling stage, attention pooling is adopted to capture most significant information with the guide of Tree LSTM (a variant of Recurrent NN) sentence representations. Comprehensive information is extracted by the pooling scheme and the combination of the convolutional layer and the tree long short term memory. We evaluate the model on sentiment classification task. Experiment results show that utilization of the given structures and combination of Tree LSTM and DCNN outperforms both Tree LSTM and DCNN and achieves outstanding performance.
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