Conversational agents leveraging AI, particularly deep learning, are emerging in both academic research and real-world applications. However, these applications still face challenges, including disrespecting knowledge and facts, not personalizing to user preferences, and enormous demand for computational resources during training and inference. Recent research efforts have been focused on addressing these challenges from various aspects, including supplementing various types of auxiliary information to the conversational agents. However, existing methods are still not able to effectively and efficiently exploit relevant information from these auxiliary supplements to further unleash the power of the conversational agents and the language models they use. In this paper, we present a novel method, PK-NCLI, that is able to accurately and efficiently identify relevant auxiliary information to improve the quality of conversational responses by learning the relevance among persona, chat history, and knowledge background through low-level normalized contextual latent interaction. Our experimental results indicate that PK-NCLI outperforms the state-of-the-art method, PK-FoCus, by 47.80%/30.61%/24.14% in terms of perplexity, knowledge grounding, and training efficiency, respectively, and maintained the same level of persona grounding performance. We also provide a detailed analysis of how different factors, including language model choices and trade-offs on training weights, would affect the performance of PK-NCLI.

翻译：利用人工智能（尤其是深度学习）的对话代理正涌现于学术研究与实际应用中。然而，这些应用仍面临诸多挑战，包括对知识事实的尊重不足、未能个性化适配用户偏好，以及训练与推理阶段对计算资源的巨大需求。近年来，研究重点聚焦于从多角度应对这些挑战，例如向对话代理补充各类辅助信息。然而，现有方法仍无法高效且有效地从这些辅助信息中提取相关数据，以进一步释放对话代理及其所使用语言模型的潜力。本文提出一种名为PK-NCLI的新方法，通过低层归一化上下文隐式交互，学习用户画像、聊天历史与知识背景之间的语义关联，从而精准高效地识别相关辅助信息，提升对话响应质量。实验结果表明，PK-NCLI在困惑度指标、知识锚定能力及训练效率上，较现有最优方法PK-FoCus分别提升47.80%、30.61%与24.14%，同时保持同等水平的用户画像锚定性能。此外，本文还详细分析了语言模型选择、训练权重权衡等不同因素对PK-NCLI性能的影响。