@InProceedings{kulkarni-EtAl:2017:I17-2,
  author    = {Kulkarni, Kaustubh  and  Togashi, Riku  and  Maeda, Hideyuki  and  Fujita, Sumio},
  title     = {Dual Constrained Question Embeddings with Relational Knowledge Bases for Simple 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     = {217--221},
  abstract  = {Embedding based approaches are shown to be effective for solving simple
	Question Answering (QA) problems in recent works. The major drawback of current
	approaches is that they look only at the similarity (constraint) between a
	question and a head, relation pair. Due to the absence of tail (answer) in the
	questions, these models often require paraphrase datasets to obtain adequate
	embeddings. 
	In this paper, we propose a dual constraint model which exploits the embeddings
	obtained by Trans* family of algorithms to solve the simple QA problem without
	using any additional resources such as paraphrase datasets. The results
	obtained prove that the embeddings learned using dual constraints are better
	than those with single constraint models having similar architecture.},
  url       = {http://www.aclweb.org/anthology/I17-2037}
}