@InProceedings{xu-EtAl:2016:COLING2,
  author    = {xu, jiaming  and  Shi, Jing  and  Yao, Yiqun  and  Zheng, Suncong  and  Xu, Bo  and  Xu, Bo},
  title     = {Hierarchical Memory Networks for Answer Selection on Unknown Words},
  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     = {2290--2299},
  abstract  = {Recently, end-to-end memory networks have shown promising results on Question
	Answering task, which encode the past facts into an explicit memory and perform
	reasoning ability by making multiple computational steps on the memory.
	However, memory networks conduct the reasoning on sentence-level memory to
	output coarse semantic vectors and do not further take any attention mechanism
	to focus on words, which may lead to the model lose some detail information,
	especially when the answers are rare or unknown words. In this paper, we
	propose a novel Hierarchical Memory Networks, dubbed HMN. First, we encode the
	past facts into sentence-level memory and word-level memory respectively. Then,
	\(k\)-max pooling is exploited following reasoning module on the sentence-level
	memory to sample the \(k\) most relevant sentences to a question and feed these
	sentences into attention mechanism on the word-level memory to focus the words
	in the selected sentences. Finally, the prediction is jointly learned over the
	outputs of the sentence-level reasoning module and the word-level attention
	mechanism. The experimental results demonstrate that our approach successfully
	conducts answer selection on unknown words and achieves a better performance
	than memory networks.},
  url       = {http://aclweb.org/anthology/C16-1216}
}