@InProceedings{kulkarni-boyer:2018:W18-05,
  author    = {Kulkarni, Mayank  and  Boyer, Kristy},
  title     = {Toward Data-Driven Tutorial Question Answering with Deep Learning Conversational Models},
  booktitle = {Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {273--283},
  abstract  = {There has been an increase in popularity of data-driven question answering systems given their recent success. This pa-per explores the possibility of building a tutorial question answering system for Java programming from data sampled from a community-based question answering forum. This paper reports on the creation of a dataset that could support building such a tutorial question answering system and discusses the methodology to create the 106,386 question strong dataset. We investigate how retrieval-based and generative models perform on the given dataset. The work also investigates the usefulness of using hybrid approaches such as combining retrieval-based and generative models. The results indicate that building data-driven tutorial systems using community-based question answering forums holds significant promise.},
  url       = {http://www.aclweb.org/anthology/W18-0532}
}