@InProceedings{pragst-EtAl:2017:I17-1,
  author    = {Pragst, Louisa  and  Yoshino, Koichiro  and  Minker, Wolfgang  and  Nakamura, Satoshi  and  Ultes, Stefan},
  title     = {Acquisition and Assessment of Semantic Content for the Generation of Elaborateness and Indirectness in Spoken Dialogue Systems},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {915--925},
  abstract  = {In a dialogue system, the dialogue manager selects one of several system
	actions and thereby determines the system's behaviour. Defining all possible
	system actions in a dialogue system by hand is a tedious work. While efforts
	have been made to automatically generate such system actions, those approaches
	are mostly focused on providing functional system behaviour. Adapting the
	system behaviour to the user becomes a difficult task due to the limited amount
	of system actions available. We aim to increase the adaptability of a dialogue
	system by automatically generating variants of system actions. In this work, we
	introduce an approach to automatically generate action variants for
	elaborateness and indirectness. Our proposed algorithm extracts RDF triplets
	from a knowledge base and rates their relevance to the original system action
	to find suitable content. We show that the results of our algorithm are mostly
	perceived similarly to human generated elaborateness and indirectness and can
	be used to adapt a conversation to the current user and situation. We also
	discuss where the results of our algorithm are still lacking and how this could
	be improved: Taking into account the conversation topic as well as the culture
	of the user is likely to have beneficial effect on the user's perception.},
  url       = {http://www.aclweb.org/anthology/I17-1092}
}