  Recursive Neural Network (RecNN), a type of models which compose words or
phrases recursively over syntactic tree structures, has been proven to have
superior ability to obtain sentence representation for a variety of NLP tasks.
However, RecNN is born with a thorny problem that a shared compositional
function for each node of trees can't capture the complex semantic
compositionality so that the expressive power of model is limited. In this
paper, in order to address this problem, we propose Tag-Guided
HyperRecNN/TreeLSTM (TG-HRecNN/TreeLSTM), which introduces hypernetwork into
RecNNs to take as inputs Part-of-Speech (POS) tags of word/phrase and generate
the semantic composition parameters dynamically. Experimental results on five
datasets for two typical NLP tasks show proposed models both obtain significant
improvement compared with RecNN and TreeLSTM consistently. Our TG-HTreeLSTM
outperforms all existing RecNN-based models and achieves or is competitive with
state-of-the-art on four sentence classification benchmarks. The effectiveness
of our models is also demonstrated by qualitative analysis.
