Dynamic Compositionality in Recursive Neural Networks with Structure-aware Tag Representations

Understand

Most existing recursive neural network (RvNN) architectures utilize only the structure of parse trees, ignoring syntactic tags which are provided as by-products of parsing.

• We present a novel RvNN architecture that can provide dynamic compositionality by considering comprehensive syntactic information derived from both the structure and linguistic tags.
• Specifically, we introduce a structure-aware tag representation constructed by a separate tag-level tree-LSTM.
• With this, we can control the composition function of the existing word-level tree-LSTM by augmenting the representation as a supplementary input to the gate functions of the tree-LSTM.