基于对比学习的双边个性化对话生成
Bilateral Personalized Dialogue Generation with Contrastive Learning
AI总结：
针对人机对话中忽视用户人格的问题，提出双边个性化对话生成方法，融合用户与机器人人格，采用条件互信息最大化与对比学习选回复，实验验证其更个性化且一致。
摘要：
生成个性化回复是自然的人机交互中的主要挑战之一。目前该领域的研究主要集中于生成与机器人预设人格一致的回复，而忽略了用户的人格。这样的回复可能不合适甚至冒犯用户，从而导致糟糕的用户体验。因此，我们提出了一种用于双人对话的双边个性化对话生成（BPDG）方法，该方法通过设计一种动态人格感知融合方法，将用户和机器人的人格整合到对话生成中。为了弥合学习目标函数与评估指标之间的差距，我们采用条件互信息最大化（CMIM）准则，并结合对比学习从生成的候选中选择适当的回复。此外，我们设计了一个双边人格准确率指标来衡量双边个性化的程度。实验结果表明，与几种最先进的方法相比，所提方法的最终结果在自动评估和人工评估方面都更具个性化，并且与双边人格更加一致。
英文摘要：
Generating personalized responses is one of the major challenges in natural human-robot interaction. Current researches in this field mainly focus on generating responses consistent with the robot's pre-assigned persona, while ignoring the user's persona. Such responses may be inappropriate or even offensive, which may lead to the bad user experience. Therefore, we propose a Bilateral Personalized Dialogue Generation (BPDG) method for dyadic conversation, which integrates user and robot personas into dialogue generation via designing a dynamic persona-aware fusion method. To bridge the gap between the learning objective function and evaluation metrics, the Conditional Mutual Information Maximum (CMIM) criterion is adopted with contrastive learning to select the proper response from the generated candidates. Moreover, a bilateral persona accuracy metric is designed to measure the degree of bilateral personalization. Experimental results demonstrate that, compared with several state-of-the-art methods, the final results of the proposed method are more personalized and consistent with bilateral personas in terms of both automatic and manual evaluations.
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。
