Core Concepts
Employing predicted latent personality dimensions and counterfactual reasoning to enhance the adaptability and effectiveness of persuasive dialogue systems.
Abstract
The paper introduces a novel approach that leverages predicted latent personality dimensions (LPDs) of users during ongoing persuasive conversations to generate tailored counterfactual utterances. This enables the system to dynamically adapt the conversation flow to better suit the evolving user traits.
The key components of the proposed architecture are:
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Estimation of Individual Latent Personality Dimensions:
- A Dialogue-based Personality Prediction Regression (DPPR) model is developed to infer the user's LPDs in real-time during the conversation.
- This allows the system to track and leverage the user's evolving personality traits to adjust the persuasive strategies.
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Counterfactual Data Generation:
- A Bi-directional Generative Adversarial Network (BiCoGAN) is employed in tandem with the DPPR model to generate counterfactual data.
- The counterfactual data provides alternative system utterances based on the predicted LPDs, expanding the original dialogue dataset.
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Policy Learning for Optimized Persuasion:
- The Dueling Double-Deep Q-Network (D3QN) model is used to learn policies on the counterfactual data, aiming to optimize the selection of system utterances and enhance the overall persuasion outcome.
Experiments conducted on the PersuasionForGood dataset demonstrate the superiority of the proposed approach over the existing BiCoGAN method. The cumulative rewards and Q-values produced by the method surpass the ground truth benchmarks, showcasing the effectiveness of employing counterfactual reasoning and LPDs to optimize reinforcement learning policy in online persuasive interactions.
Stats
The PersuasionForGood dataset contains 1,017 dialogues, with 545 (54%) recorded as donors and 472 (46%) as non-donors.
The OCEAN personality traits of the persuadees are provided as 5-dimensional vectors.
Quotes
"Customizing persuasive conversations related to the outcome of interest for specific users achieves better persuasion results."
"Existing persuasive conversation systems rely on persuasive strategies and encounter challenges in dynamically adjusting dialogues to suit the evolving states of individual users during interactions."