@InProceedings{rojasbarahona-EtAl:2016:COLING,
  author    = {Rojas Barahona, Lina M.  and  Gasic, Milica  and  Mrk\v{s}i\'{c}, Nikola  and  Su, Pei-Hao  and  Ultes, Stefan  and  Wen, Tsung-Hsien  and  Young, Steve},
  title     = {Exploiting Sentence and Context Representations in Deep Neural Models for Spoken Language Understanding},
  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     = {258--267},
  abstract  = {This paper presents a deep learning architecture for the semantic decoder
	component of a Statistical Spoken Dialogue System. In a slot-filling dialogue,
	the semantic decoder predicts the dialogue act and a set of slot-value pairs
	from a set of n-best hypotheses returned by the Automatic Speech Recognition. 
	Most current  models for spoken language understanding assume (i) word-aligned
	semantic annotations as in sequence taggers and (ii) delexicalisation, or a
	mapping of input words to domain-specific concepts using heuristics that  try
	to capture morphological variation but that do not scale to other domains nor
	to language variation (e.g., morphology, synonyms, paraphrasing ). In this work
	the semantic decoder is trained using unaligned semantic annotations and it
	uses distributed semantic representation learning to overcome the limitations
	of explicit delexicalisation.  The proposed  architecture uses a convolutional
	neural network for the sentence representation and a
	long-short term memory network for the context representation. Results are
	presented for the publicly available DSTC2 corpus and an In-car corpus which is
	similar to DSTC2 but has a significantly higher word error rate (WER).},
  url       = {http://aclweb.org/anthology/C16-1025}
}