从多语言复杂性到情感清晰度：利用常识揭示混合语言对话中的情感
From Multilingual Complexity to Emotional Clarity: Leveraging Commonsense to Unveil Emotions in Code-Mixed Dialogues
AI总结：
本研究针对混合语言对话情感识别问题，提出一种整合常识信息与对话上下文的创新方法，通过高效流水线提取常识并采用先进融合技术结合对话表示，实验证明该方法显著提升了情感识别性能。
摘要：
理解对话中的情感是人类交流的基础，推动了对话情感识别（ERC）的NLP研究。尽管已有大量研究关注单语对话中单个说话者的情感识别，但对混合语言对话中情感动态的理解却相对较少。这促使我们在本研究中开展针对混合语言对话的ERC研究。认识到情感智能包含对世界知识的理解，我们提出了一种创新方法，将常识信息与对话上下文相结合，以促进对情感的更深入理解。为此，我们设计了一个高效的流水线，基于混合语言输入从现有知识图谱中提取相关常识。随后，我们开发了一种先进的融合技术，将获取的常识信息与从专用对话理解模块获得的对话表示无缝结合。我们的全面实验展示了通过系统性地将常识纳入ERC所获得的显著性能提升。定量评估和定性分析进一步证实了我们的假设，再次肯定了常识整合在增强ERC中的关键作用。
英文摘要：
Understanding emotions during conversation is a fundamental aspect of human communication, driving NLP research for Emotion Recognition in Conversation (ERC). While considerable research has focused on discerning emotions of individual speakers in monolingual dialogues, understanding the emotional dynamics in code-mixed conversations has received relatively less attention. This motivates our undertaking of ERC for code-mixed conversations in this study. Recognizing that emotional intelligence encompasses a comprehension of worldly knowledge, we propose an innovative approach that integrates commonsense information with dialogue context to facilitate a deeper understanding of emotions. To achieve this, we devise an efficient pipeline that extracts relevant commonsense from existing knowledge graphs based on the code-mixed input. Subsequently, we develop an advanced fusion technique that seamlessly combines the acquired commonsense information with the dialogue representation obtained from a dedicated dialogue understanding module. Our comprehensive experimentation showcases the substantial performance improvement obtained through the systematic incorporation of commonsense in ERC. Both quantitative assessments and qualitative analyses further corroborate the validity of our hypothesis, reaffirming the pivotal role of commonsense integration in enhancing ERC.
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。
