@InProceedings{p-garg-shevade:2017:EMNLP2017,
  author    = {P, Deepak  and  Garg, Dinesh  and  Shevade, Shirish},
  title     = {Latent Space Embedding for Retrieval in Question-Answer Archives},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {855--865},
  abstract  = {Community-driven Question Answering (CQA) systems such as Yahoo! Answers have
	become valuable sources of reusable information. CQA retrieval enables usage of
	historical CQA archives to solve new questions posed by users. This task has
	received much recent attention, with methods building upon literature from
	translation models, topic models, and deep learning. In this paper, we devise a
	CQA retrieval technique, LASER-QA, that embeds question-answer pairs within a
	unified latent space preserving the local neighborhood structure of question
	and answer spaces. The idea is that such a space mirrors semantic similarity
	among questions as well as answers, thereby enabling high quality retrieval.
	Through an empirical analysis on various real-world QA datasets, we illustrate
	the improved effectiveness of LASER-QA over state-of-the-art methods.},
  url       = {https://www.aclweb.org/anthology/D17-1089}
}