  In this paper, we propose a hybrid technique for semantic question matching.
It uses our proposed two-layered taxonomy for English questions by augmenting
state-of-the-art deep learning models with question classes obtained from a
deep learning based question classifier. Experiments performed on three
open-domain datasets demonstrate the effectiveness of our proposed approach. We
achieve state-of-the-art results on partial ordering question ranking (POQR)
benchmark dataset. Our empirical analysis shows that coupling standard
distributional features (provided by the question encoder) with knowledge from
taxonomy is more effective than either deep learning (DL) or taxonomy-based
knowledge alone.
