@InProceedings{chen-bunescu:2017:I17-2,
  author    = {Chen, Charles  and  Bunescu, Razvan},
  title     = {An Exploration of Data Augmentation and RNN Architectures for Question Ranking in Community Question Answering},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {442--447},
  abstract  = {The automation of tasks in community question answering (cQA) is dominated by
	machine learning approaches, whose performance is often limited by the number
	of training examples. Starting from a neural sequence learning approach with
	attention, we explore the impact of two data augmentation techniques on
	question ranking performance: a method that swaps reference questions with
	their paraphrases, and training on examples automatically selected from
	external datasets. Both methods are shown to lead to substantial gains in
	accuracy over a strong baseline. Further improvements are obtained by changing
	the model architecture to mirror the structure seen in the data.},
  url       = {http://www.aclweb.org/anthology/I17-2075}
}