@InProceedings{akasaki-kaji:2017:Long,
  author    = {Akasaki, Satoshi  and  Kaji, Nobuhiro},
  title     = {Chat Detection in an Intelligent Assistant: Combining Task-oriented and Non-task-oriented Spoken Dialogue Systems},
  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     = {1308--1319},
  abstract  = {Recently emerged intelligent assistants on smartphones and home electronics
	(e.g., Siri and Alexa) can be seen as novel hybrids of domain-specific
	task-oriented spoken dialogue systems and open-domain non-task-oriented ones.
	To realize such hybrid dialogue systems, this paper investigates determining
	whether or not a user is going to have a chat with the system. To address the
	lack of benchmark datasets for this task, we construct a new dataset consisting
	of 15,160 utterances collected from the real log data of a commercial
	intelligent assistant (and will release the dataset to facilitate future
	research activity). In addition, we investigate using tweets and Web search
	queries for handling open-domain user utterances, which characterize the task
	of chat detection. Experimental experiments demonstrated that, while simple
	supervised methods are effective, the use of the tweets and search queries
	further improves the F$\_1$-score from 86.21 to 87.53.},
  url       = {http://aclweb.org/anthology/P17-1120}
}