Discriminative Neural Sentence Modeling by Tree-Based Convolution

Understand

This paper proposes a tree-based convolutional neural network (TBCNN) for discriminative sentence modeling.

• Our models leverage either constituency trees or dependency trees of sentences.
• The tree-based convolution process extracts sentences' structural features, and these features are aggregated by max pooling.
• Such architecture allows short propagation paths between the output layer and underlying feature detectors, which enables effective structural feature learning and extraction.