Entropy-Driven Dialog for Topic Classification: Detecting and Tackling Uncertainty

@Article {
title = {Entropy-Driven Dialog for Topic Classification: Detecting and Tackling Uncertainty},
pages = {171-182},
volume = {427},
keywds = {

Dialog System, System Turn Generation, Uncertainty Detection, Information Recovery

}
abstract = {

A frequent difficulty faced by developers of Dialogue Systems is the absence of a corpus of conversations to model the dialog statistically. Even when such a corpus is available, neither an agenda- nor a statistically-based dialog control logic are options if the domain knowledge is broad. This paper presents a module that automatically generates system-turn utterances to guide the user through the dialog. These system-turns are not established beforehand, and vary with each dialog. The module is valid for agenda-based and statistical approaches, being applicable in both types of corpora. Particularly, the task defined in this paper is the automation of a call-routing service. The proposed module is used when the user has not given enough information to route the call with high confidence. Doing so, and using the generated system-turns, the obtained information is improved through the dialog. The paper focuses on the development and operation of this module.

}
isbn = {978-981-10-2584-6},
isi = {1},
doi = {10.1007/978-981-10-2585-3_13},
date = {2017-01-01},
year = {2017},
}