Deep Recursive Neural Networks for Compositionality in Language

Irsoy, Ozan, Cardie, Claire

Neural Information Processing Systems 

Recursive neural networks comprise a class of architecture that can operate on structured input. They have been previously successfully applied to model compositionality in natural language using parse-tree-based structural representations. Even though these architectures are deep in structure, they lack the capacity for hierarchical representation that exists in conventional deep feed-forward networks as well as in recently investigated deep recurrent neural networks. In this work we introduce a new architecture --- a deep recursive neural network (deep RNN) --- constructed by stacking multiple recursive layers. We evaluate the proposed model on the task of fine-grained sentiment classification.