使用预测的潜在人格维度进行反事实推理以优化说服结果
Counterfactual Reasoning Using Predicted Latent Personality Dimensions for Optimizing Persuasion Outcome
AI总结：
提出基于潜在人格维度（LPDs）和反事实推理的新方法，利用BiCoGAN与DPPR生成反事实数据，并用D3QN学习话语选择策略，在PersuasionForGood数据集上验证了其优化说服结果的有效性。
摘要：
针对特定用户定制与目标结果相关的说服性对话能取得更好的说服效果。然而，现有的说服性对话系统依赖说服策略，在动态调整对话以适应交互过程中个体用户不断演变的状态方面面临挑战。这一局限性限制了系统提供灵活或动态对话的能力，导致说服结果欠佳。在本文中，我们提出了一种新方法，在持续的说服对话中跟踪用户的潜在人格维度（LPDs），并基于这些LPDs生成量身定制的反事实话语，以优化整体说服结果。具体而言，我们提出的方法利用双向生成对抗网络（BiCoGAN）与基于对话的人格预测回归（DPPR）模型相结合来生成反事实数据。这使系统能够构建更适合用户的替代性说服话语。随后，我们利用D3QN模型在反事实数据上学习优化系统话语选择的策略。我们使用PersuasionForGood数据集获得的实验结果证明了我们的方法优于现有方法BiCoGAN。我们的方法产生的累积奖励和Q值超越了真实基准，展示了在在线交互中采用反事实推理和LPDs优化强化学习策略的有效性。
英文摘要：
Customizing persuasive conversations related to the outcome of interest for specific users achieves better persuasion results. However, 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. This limitation restricts the system's ability to deliver flexible or dynamic conversations and achieve suboptimal persuasion outcomes. In this paper, we present a novel approach that tracks a user's latent personality dimensions (LPDs) during ongoing persuasion conversation and generates tailored counterfactual utterances based on these LPDs to optimize the overall persuasion outcome. In particular, our proposed method leverages a Bi-directional Generative Adversarial Network (BiCoGAN) in tandem with a Dialogue-based Personality Prediction Regression (DPPR) model to generate counterfactual data. This enables the system to formulate alternative persuasive utterances that are more suited to the user. Subsequently, we utilize the D3QN model to learn policies for optimized selection of system utterances on counterfactual data. Experimental results we obtained from using the PersuasionForGood dataset demonstrate the superiority of our approach over the existing method, BiCoGAN. The cumulative rewards and Q-values produced by our method surpass ground truth benchmarks, showcasing the efficacy of employing counterfactual reasoning and LPDs to optimize reinforcement learning policy in online interactions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
