@InProceedings{vougiouklis-hare-simperl:2016:COLING,
  author    = {Vougiouklis, Pavlos  and  Hare, Jonathon  and  Simperl, Elena},
  title     = {A Neural Network Approach for Knowledge-Driven Response Generation},
  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     = {3370--3380},
  abstract  = {We present a novel response generation system. The system assumes the
	hypothesis that participants in a conversation base their response not only on
	previous dialog utterances but also on their background knowledge. Our model is
	based on a Recurrent Neural Network (RNN) that is trained over concatenated
	sequences of comments, a Convolution Neural Network that is trained over
	Wikipedia sentences and a formulation that couples the two trained embeddings
	in a multimodal space. We create a dataset of aligned Wikipedia sentences and
	sequences of Reddit utterances, which we we use to train our model. Given a
	sequence of past utterances and a set of sentences that represent the
	background knowledge, our end-to-end learnable model is able to generate
	context-sensitive and knowledge-driven responses by leveraging the alignment of
	two different data sources. Our approach achieves up to 55% improvement in
	perplexity compared to purely sequential models based on RNNs that are trained
	only on sequences of utterances.},
  url       = {http://aclweb.org/anthology/C16-1318}
}