@inproceedings{zhai2014discovering,
author = {Zhai, Ke and Williams, Jason},
title = {Discovering Latent Structure in Task-Oriented Dialogues},
booktitle = {Proceedings of ACL 2014},
year = {2014},
month = {June},
abstract = {A key challenge for computational conversation models is to discover latent structure in task-oriented dialogue, since it provides a basis for analysing, evaluating, and building conversational systems. We propose three new unsupervised models to discover latent structures in task-oriented dialogues. Our methods synthesize hidden Markov models (for underlying state) and topic models (to connect words to states). We apply them to two real, non-trivial datasets: human-computer spoken dialogues in bus query service, and human-human text-based chats from a live technical support service. We show that our models extract meaningful state representations and dialogue structures consistent with human annotations. Quantitatively, we show our models achieve superior performance on held-out log likelihood evaluation and an ordering task.},
publisher = {Association for Computational Linguistics},
url = {http://approjects.co.za/?big=en-us/research/publication/discovering-latent-structure-in-task-oriented-dialogues/},
edition = {Proceedings of ACL 2014},
}