  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. In extensive experiments, we show that
models built upon the proposed architecture obtain superior or competitive
performance on several sentence-level tasks such as sentiment analysis and
natural language inference when compared against previous tree-structured
models and other sophisticated neural models.
