@InProceedings{hao-EtAl:2017:Long,
  author    = {Hao, Yanchao  and  Zhang, Yuanzhe  and  Liu, Kang  and  He, Shizhu  and  Liu, Zhanyi  and  Wu, Hua  and  Zhao, Jun},
  title     = {An End-to-End Model for Question Answering over Knowledge Base with Cross-Attention Combining Global Knowledge},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {221--231},
  abstract  = {With the rapid growth of knowledge bases (KBs) on the web, how to take full
	advantage of them becomes increasingly important. Question answering over
	knowledge base (KB-QA) is one of the  promising approaches to access the
	substantial knowledge. Meanwhile, as the neural network-based (NN-based)
	methods develop, NN-based KB-QA has already achieved impressive results.
	However, previous work did not put more emphasis on question representation,
	and the question is converted into a fixed vector regardless of its candidate
	answers. This simple representation strategy is not easy to express the proper
	information in the question. Hence, we present an end-to-end neural network
	model to represent the questions and their corresponding scores dynamically
	according to the various candidate answer aspects via cross-attention
	mechanism. In addition, we leverage the global knowledge inside the underlying
	KB, aiming at integrating the rich KB information into the representation of
	the answers. As a result, it could alleviates the out-of-vocabulary (OOV)
	problem, which helps the cross-attention model to represent the question more
	precisely. The experimental results on WebQuestions demonstrate the
	effectiveness of the proposed approach.},
  url       = {http://aclweb.org/anthology/P17-1021}
}