@InProceedings{wang-tan-han:2016:COLING,
  author    = {Wang, Lidan  and  Tan, Ming  and  Han, Jiawei},
  title     = {FastHybrid: A Hybrid Model for Efficient Answer Selection},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {2378--2388},
  abstract  = {Answer selection is a core component in any question-answering systems. It aims
	to select correct answer sentences for a given question from a pool of
	candidate sentences. In recent years, many deep learning methods have been
	proposed and shown excellent results for this task. However, these methods
	typically require extensive parameter (and hyper-parameter) tuning, which give
	rise to efficiency issues for large-scale datasets, and potentially make them
	less portable across new datasets and domains (as re-tuning is usually
	required). In this paper, we propose an extremely efficient hybrid model
	(FastHybrid) that tackles the problem from both an accuracy and scalability
	point of view. FastHybrid is a light-weight model that requires little tuning
	and adaptation across different domains. It combines a fast deep model (which
	will be introduced in the method section) with an initial information retrieval
	model to effectively and efficiently handle answer selection. We introduce a
	new efficient attention mechanism in the hybrid model and demonstrate its
	effectiveness on several QA datasets. Experimental results show that although
	the hybrid uses no training data, its accuracy is often on-par with supervised
	deep learning techniques, while significantly reducing training and tuning
	costs across different domains.},
  url       = {http://aclweb.org/anthology/C16-1224}
}