Generation and evaluation of user tailored responses in multimodal dialogue

Cognitive Science 28 (5):811-840 (2004)
  Copy   BIBTEX

Abstract

When people engage in conversation, they tailor their utterances to their conversational partners, whether these partners are other humans or computational systems. This tailoring, or adaptation to the partner takes place in all facets of human language use, and is based on a mental model or a user model of the conversational partner. Such adaptation has been shown to improve listeners' comprehension, their satisfaction with an interactive system, the efficiency with which they execute conversational tasks, and the likelihood of achieving higher level goals such as changing the listener's beliefs and attitudes. We focus on one aspect of adaptation, namely the tailoring of the content of dialogue system utterances for the higher level processes of persuasion, argumentation and advice-giving. Our hypothesis is that algorithms that adapt content for these processes, according to a user model, will improve the usability, efficiency, and effectiveness of dialogue systems. We describe a multimodal dialogue system and algorithms for adaptive content selection based on multi-attribute decision theory. We demonstrate experimentally the improved efficacy of system responses through the use of user models to both tailor the content of system utterances and to manipulate their conciseness.

Links

PhilArchive



    Upload a copy of this work     Papers currently archived: 93,642

External links

Setup an account with your affiliations in order to access resources via your University's proxy server

Through your library

Similar books and articles

個人の推薦に基づく個人間情報共有モデル.船越 要 亀井 剛次 - 2004 - Transactions of the Japanese Society for Artificial Intelligence 19 (6):540-547.

Analytics

Added to PP
2013-11-21

Downloads
9 (#449,242)

6 months
47 (#327,594)

Historical graph of downloads
How can I increase my downloads?

Author Profiles

Citations of this work

Generating and evaluating evaluative arguments.Giuseppe Carenini & Johanna D. Moore - 2006 - Artificial Intelligence 170 (11):925-952.

Add more citations