Dynamic Compositional Neural Networks over Tree Structure

Dynamic Compositional Neural Networks over Tree Structure

Pengfei Liu, Xipeng Qiu, Xuanjing Huang

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Main track. Pages 4054-4060. https://doi.org/10.24963/ijcai.2017/566

Tree-structured neural networks have proven to be effective in learning semantic representations by exploitingsyntactic information. In spite of their success, most existing models suffer from the underfitting problem: they recursively use the same shared compositional function throughout the whole compositional process and lack expressive power due to inability to capture the richness of compositionality.In this paper, we address this issue by introducing the dynamic compositional neural networks over tree structure (DC-TreeNN), in which the compositional function is dynamically generated by a meta network.The role of meta-network is to capture the metaknowledge across the different compositional rules and formulate them. Experimental results on two typical tasks show the effectiveness of the proposed models.
Keywords:
Natural Language Processing: Natural Language Semantics
Natural Language Processing: Natural Language Processing