“Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good” Paper Summary & Analysis

Objectives / Goals of the Paper

The main objective of this paper is to develop the foundations for personalized persuasive conversational agents to change people’s opinions and actions for social good. In the paper, the authors create a classifier to predict persuasive strategies as the groundwork for future research in this area. Although there have already been previously published papers looking into learning better negotiation strategies, Persuasion for Good distinguishes itself through its emphasis on the personalization aspect of the persuasion process. By taking in user background information through sub-questionnaires such as the Big Five personality test alongside conversational data, the paper aims to find relationships between users’ backgrounds and the effectiveness of persuasion strategies.

Paper Contributions

In the paper, the authors collected conversational data of participants trying to convince other participants to donate to a charity using Amazon’s Mechanical Turk. They then annotated the data to indicate the presence of certain persuasion techniques. This allowed for analysis of psychological factors, as well as the persuasion technique being used. Model-wise, they used a hybrid RCNN model which included combinations of different embeddings, ranging from character level to context level embedding. They use this classifier on their annotated data to predict the presence of 10 persuasive strategies.

Source: https://arxiv.org/pdf/1906.06725.pdf
Source: https://arxiv.org/pdf/1906.06725.pdf

Paper Limitations

The approach seems somewhat limited in that some arguments may be constructed via some combination of potential approaches. For example, an argument may appeal both to the emotional and logical sensibilities of the persuadee, but the dataset assumedly only allows arguments of exactly one strategy. This is addressed in section 6, but simply assigning the most salient strategy used as the classification of the argument seems limited and prone to noise.

Further Research

Future research can look into how the model can be implemented in practice (how user information should be collected, how to make sure the ethical guidelines mentioned in the paper will be enforced, etc.). The authors also suggest gathering more annotated data and more dialogue context to improve the performance of their classifiers.

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Cornell Data Science

Cornell Data Science

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