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Chatbots for CRM and Dialogue Management Read chapter Read first chapter

2021 | OriginalPaper | Chapter

4. A Virtual Social Promotion Chatbot with Persuasion and Rhetorical Coordination

Author : Boris Galitsky

Published in: Artificial Intelligence for Customer Relationship Management

Publisher: Springer International Publishing

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Abstract

We build a chatbot that delivers content in the form of virtual dialogues automatically produced from documents. Given an initial query, this chatbot finds documents, extracts topics from them, organizes these topics in clusters, receives from the user clarification on which cluster is most relevant, and provides the content for this cluster. This content can be provided in the form of a virtual dialogue so that the answers are derived from the found documents by splitting it, and questions are automatically generated for these answers. Virtual dialogues as search results turn out to be more effective means of information access in comparison with original document chunks provided by a conventional chatbot. To support the natural flow of a conversation in a chatbot, the rhetorical structure of each message had to be analyzed. We classify a pair of paragraphs of text as appropriate for one to follow another, or inappropriate, based on communicative discourse considerations. We then describe a chatbot performing advertising and social promotion (CASP) to assist in the automation of managing friends and other social network contacts. This agent employs a domain-independent natural language relevance technique that filters web-mining results to support a conversation with friends. This technique relies on learning parse trees and parses thickets (sets of parse trees) of paragraphs of text such as Facebook postings. We evaluate CASP in a number of domains, acting on behalf of its human host. Although some Facebook friends did not like CASP postings and even unfriended the host, overall social promotion results are positive as long as relevance, style and rhetorical appropriateness are properly maintained. Finally, we propose a way to improve a discourse parsing by a refinement of default rhetorical relations, based on an analysis of Abstract Meaning Representation (AMR). A number of AMR semantic relations such as Contrast can be used to detect a specific rhetorical relation.

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previous chapter Adjusting Chatbot Conversation to User Personality and Mood
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Metadata
Title
A Virtual Social Promotion Chatbot with Persuasion and Rhetorical Coordination
Author
Boris Galitsky
Copyright Year
2021
Book
Artificial Intelligence for Customer Relationship Management

Print ISBN: 978-3-030-61640-3

Electronic ISBN: 978-3-030-61641-0

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-61641-0_4