UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

A persuasive chatbot using a crowd-sourced argument graph and concerns

Chalaguine, LA; Hunter, A; (2020) A persuasive chatbot using a crowd-sourced argument graph and concerns. In: Computational Models of Argument. (pp. pp. 9-20). IOS Press Green open access

[img]
Preview
Text
FAIA-326-FAIA200487.pdf - Published version

Download (266kB) | Preview

Abstract

Chatbots are versatile tools that have the potential of being used for computational persuasion where the chatbot acts as the persuader and the human agent as the persuadee. To allow the user to type his or her arguments, as opposed to selecting them from a menu, the chatbot needs a sufficiently large knowledge base of arguments and counterarguments. And in order to make the user change their current stance on a subject, the chatbot needs a method to select persuasive counterarguments. To address this, we present a chatbot that is equipped with an argument graph and the ability to identify the concerns of the user argument in order to select appropriate counterarguments. We evaluate the bot in a study with participants and show how using our method can make the chatbot more persuasive.

Type: Proceedings paper
Title: A persuasive chatbot using a crowd-sourced argument graph and concerns
Open access status: An open access version is available from UCL Discovery
DOI: 10.3233/FAIA200487
Publisher version: https://doi.org/10.3233/FAIA200513
Language: English
Additional information: This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0). See: https://creativecommons.org/licenses/by-nc/4.0/deed.en_US
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10113512
Downloads since deposit
115Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item