提出新任务与框架，让模型识别对话中从未见过的情绪。
Emotion Transfer with Enhanced Prototype for Unseen Emotion Recognition in Conversation
- 用大模型增强描述原型，解决情绪定义模糊问题。
- 设计无参数编码机制，提升长对话情绪建模能力。
- 改进注意力维特比解码，迁移已知情绪转移模式至未知情绪。
当前对话情绪识别（ERC）研究遵循封闭域假设，但心理学界对情绪分类尚未达成共识，导致模型在真实场景中难以识别未见过的情绪。为此，我们首次提出对话中未见情绪识别（UERC）任务，并提出基于原型的情感迁移框架ProEmoTrans。该方法应对三大挑战：第一，隐含表达使情绪定义复杂，我们采用大语言模型增强描述的方法缓解；第二，长对话中的话语编码困难，提出无参数机制实现高效编码并防止过拟合；第三，情绪具有马尔可夫流转特性，难以迁移，我们通过改进的注意力维特比解码（AVD）方法，将已知情绪转移模式迁移到未知情绪。在三个数据集上的大量实验表明，本方法为该新兴领域提供了强有力的基线基准。
原文摘要 · Abstract (English)
Current Emotion Recognition in Conversation (ERC) research follows a closed-domain assumption. However, there is no clear consensus on emotion classification in psychology, which presents a challenge for models when it comes to recognizing previously unseen emotions in real-world applications. To bridge this gap, we introduce the Unseen Emotion Recognition in Conversation (UERC) task for the first time and propose ProEmoTrans, a solid prototype-based emotion transfer framework. This prototype-based approach shows promise but still faces key challenges: First, implicit expressions complicate emotion definition, which we address by proposing an LLM-enhanced description approach. Second, utterance encoding in long conversations is difficult, which we tackle with a proposed parameter-free mechanism for efficient encoding and overfitting prevention. Finally, the Markovian flow nature of emotions is hard to transfer, which we address with an improved Attention Viterbi Decoding (AVD) method to transfer seen emotion transitions to unseen emotions. Extensive experiments on three datasets show that our method serves as a strong baseline for preliminary exploration in this new area.
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