Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks

In general, natural language is governed by a tree structure: smaller units (e.g., phrases) are nested within larger units (e.g., clauses). This is a strict hierarchy: when a larger constituent ends, all of the smaller constituents that are nested within it must also be closed. While the standard LSTM allows different neurons to track information at different time scales, the architecture does not impose a strict hierarchy. This paper proposes to add such a constraint to the system by ordering the neurons; a vector of "master" input and forget gates ensure that when a given unit is updated, all of the units that follow it in the ordering are also updated. To this end, we propose a new RNN unit: ON-LSTM, which achieves good performance on four different tasks: language modeling, unsupervised parsing, targeted syntactic evaluation, and logical inference.