CET2：为连贯且引人入胜的知识 grounded 对话建模话题转换
CET2: Modelling Topic Transitions for Coherent and Engaging Knowledge-Grounded Conversations
AI总结：
本文提出 CET2 框架，通过建模有效话题转换逻辑并比较知识候选，平衡上下文连贯性与知识多样性，从而提升知识选择的准确率与跨话题泛化能力。
摘要：
知识 grounded 对话系统旨在根据对话上下文和所选外部知识生成连贯且引人入胜的回复。以往的知识选择方法往往过度依赖对话上下文，或过分强调所选知识中的新信息，导致选择重复或不协调的知识，并进一步生成重复或不连贯的回复，因为回复的生成依赖于所选择的知识。为解决这些不足，我们提出了一个连贯且引人入胜的话题转换（Coherent and Engaging Topic Transition，CET2）框架，用于建模话题转换，以选择与对话上下文连贯、同时为话题发展提供充分知识多样性的知识。我们的 CET2 框架在知识选择中考虑多个因素，包括从对话上下文到后续话题的有效转换逻辑，以及可用知识候选之间的系统性比较。在两个公开基准上进行的大量实验证明了 CET2 在知识选择方面的优越性和更好的泛化能力。这得益于我们精心设计的转换特征和比较式知识选择策略，它们更易迁移到关于未见过话题的对话中。对细粒度知识选择准确率的分析也表明，与现有方法相比，CET2 能更好地平衡对话中的话题蕴含（上下文连贯性）与发展（知识多样性）。
英文摘要：
Knowledge-grounded dialogue systems aim to generate coherent and engaging responses based on the dialogue contexts and selected external knowledge. Previous knowledge selection methods tend to rely too heavily on the dialogue contexts or over-emphasize the new information in the selected knowledge, resulting in the selection of repetitious or incongruous knowledge and further generating repetitive or incoherent responses, as the generation of the response depends on the chosen knowledge. To address these shortcomings, we introduce a Coherent and Engaging Topic Transition (CET2) framework to model topic transitions for selecting knowledge that is coherent to the context of the conversations while providing adequate knowledge diversity for topic development. Our CET2 framework considers multiple factors for knowledge selection, including valid transition logic from dialogue contexts to the following topics and systematic comparisons between available knowledge candidates. Extensive experiments on two public benchmarks demonstrate the superiority and the better generalization ability of CET2 on knowledge selection. This is due to our well-designed transition features and comparative knowledge selection strategy, which are more transferable to conversations about unseen topics. Analysis of fine-grained knowledge selection accuracy also shows that CET2 can better balance topic entailment (contextual coherence) and development (knowledge diversity) in dialogue than existing approaches.
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。
