@article{Purver.Hough.Howes_TopiCS_2018,
author = "Matthew Purver and Julian Hough and Christine Howes",
abstract = "Miscommunication phenomena such as repair in dialogue are important indicators of the quality of communication. Automatic detection is therefore a key step towards tools which can characterize communication quality, and thus help in applications from call centre management to mental health monitoring. However, most existing computational linguistic approaches to these phenomena are insufficient for these purposes: although models of other-repair are common in human-computer dialogue systems, they tend to focus on repair initiation by systems, missing the range of repair and repair initiation forms used by humans; and while self-repair models for speech recognition and understanding are advanced, they tend to focus on removal of "disfluent" material important for full understanding of the discourse contribution. We explain the requirements for more satisfactory models, including strict incrementality of processing, and introduce initial versions for other- and self-repair with promising results.",
issn = "1756-8765",
journal = "Topics in Cognitive Science",
number = "2",
pages = "425--451",
title = "{C}omputational models of miscommunication phenomena",
url = "http://onlinelibrary.wiley.com/doi/10.1111/tops.12324/full",
volume = "10",
year = "2018",
}