AI 中文总结
研究移情响应生成问题，提出含三个互补模块的动态常识协调框架DCC，能整合常识表示、过滤低相关性关系及动态检索记忆，实验表明其可提升情绪分类准确率、响应多样性等，生成更好的响应。
AI 中文摘要
移情响应生成（ERG）要求模型识别用户情绪并生成移情响应。常识知识已被证明有助于此类推理，但现有方法通常在理解和生成过程中重复使用固定的常识表示，限制了它们在不同阶段协调此类知识的能力。我们提出了DCC，一个动态常识协调框架，具有三个互补模块：基于残差的常识交互（SCE-AttnRes）以整合上下文和情境常识表示、关联引导的常识过滤（AGCF）以降低低相关性常识关系的权重、迭代常识感知解码（ICAD）以在生成过程中动态检索常识记忆。在移情对话基准上的实验表明，DCC在保持可比困惑度的同时，比CEM基线提高了情绪分类准确率和响应多样性。基于大语言模型的盲评进一步表明，DCC生成的响应具有更好的相关性、连贯性和信息性。代码和实现细节将在https://这个网址公开提供。
英文摘要
Empathetic Response Generation (ERG) requires models to recognize users' emotions and generate empathetic responses. Commonsense knowledge has been shown to support such reasoning, yet existing approaches typically reuse fixed commonsense representations across understanding and generation, limiting their ability to coordinate such knowledge across different stages. We propose DCC, a Dynamic Commonsense Coordination Framework with three complementary modules: residual-based commonsense interaction (SCE-AttnRes) to integrate contextual and situational commonsense representations, Association-Guided Commonsense Filtering (AGCF) to down-weight low-relevance commonsense relations, and Iterative Commonsense-Aware Decoding (ICAD) to dynamically retrieve commonsense memories during generation. Experiments on the Empathetic-Dialogues benchmark show that DCC improves emotion classification accuracy and response diversity over the CEM baseline while maintaining comparable perplexity. An LLM-based blind evaluation further demonstrates that DCC generates responses with better relevance, coherence, and informativeness. The code and implementation details will be publicly available at https://github.com/Hanabi-Q/DCC-ERG.
