Paper Info
Title
Predicting Success of a Persuasion through Joint Modeling of Utterance Categorization
Abstract
BSTRACTPersuasive conversation leverages conversational strategies by the persuader to change the attitude or behavior of a persuadee towards achieving a specific goal. It involves understanding the linguistic and cognitive principles underlying the organization of strategic disclosures and appeals employed in human persuasion. One of the main challenges of such conversation is the inability of a persuader to detect the outcome of their conversation during the interaction. Such prior knowledge can help a persuader to change their conversation strategy and pre-empt possible conversation failures. In this paper, we propose a technique that analyses conversations to predict whether the persuader is going to successfully persuade the persuadee. We propose a joint model of latent utterance categorization to predict the success or the failure of a persuasive conversation. This latent categorization allows the model to identify high-level conversational contexts that influence patterns of language in a persuasive conversation. We evaluate the performance of our model on an openly available dataset. Our preliminary results demonstrate that the proposed model outperforms competitive baselines.
Year
DOI
Venue
2021
10.1145/3459637.3482160
Conference on Information and Knowledge Management
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Manjira Sinha12212.94
Tirthankar Dasgupta27626.41