@InProceedings{gupta-EtAl:2018:C18-11,
  author    = {Gupta, Deepak  and  Pujari, Rajkumar  and  Ekbal, Asif  and  Bhattacharyya, Pushpak  and  Maitra, Anutosh  and  Jain, Tom  and  Sengupta, Shubhashis},
  title     = {Can Taxonomy Help? Improving Semantic Question Matching using Question Taxonomy},
  booktitle = {Proceedings of the 27th International Conference on Computational Linguistics},
  month     = {August},
  year      = {2018},
  address   = {Santa Fe, New Mexico, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {499--513},
  abstract  = {In this paper, we propose a hybrid technique for semantic question matching. It uses a 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 or taxonomy-based knowledge alone.},
  url       = {http://www.aclweb.org/anthology/C18-1042}
}