AtmosERC：面向对话情感识别的对话级情感氛围建模
AtmosERC: Modeling Dialogue-Level Affective Atmosphere for Emotion Recognition in Conversation
AI总结：
本文提出AtmosERC框架，通过建模对话级情感氛围提升对话情感识别性能，其可作为插件线索增强基于大语言模型的ERC，且在局部情感偏差下预测更稳定。
AI中文摘要：
对话情感识别（ERC）旨在预测对话中各语句的情感，已通过以上下文为中心的建模取得显著进展。然而，全局上下文是异质信号，并非所有上下文信息都与情感预测同等相关。本文聚焦于该信号中面向情感的组成部分，即对话级情感氛围，其捕捉对话情感模式中普遍反映的潜在倾向。为估计并利用该倾向，我们提出AtmosERC，一种基于图的ERC框架，将每个对话建模为语句与说话人构成的对话图。关系感知图提取器过滤并融合异质图信号，生成对话级和说话人条件化的情感先验。所得紧凑先验可指导轻量型序列情感预测，也可转化为提示级线索用于基于大语言模型（LLM）的ERC，且无需修改骨干模型。在四个ERC基准上的实验表明，AtmosERC可提升轻量型ERC性能，作为插件线索增强基于LLM的ERC，且在局部情感偏差下能产生更稳定的预测结果。
英文摘要：
Emotion Recognition in Conversation (ERC) aims to predict utterance-level emotions in dialogues and has largely advanced through context-centric modeling. However, global context is a heterogeneous signal, and not all contextual information is equally relevant to emotion prediction. This paper focuses on the affect-oriented component of this signal, termed dialogue-level affective atmosphere, which captures a latent tendency commonly reflected in conversational emotion patterns. To estimate and exploit this tendency, we propose AtmosERC, a graph-based ERC framework that models each dialogue as a conversational graph over utterances and speakers. A relation-aware graph extractor filters and fuses heterogeneous graph signals to produce dialogue-level and speaker-conditioned affective priors. The resulting compact prior guides lightweight sequential emotion prediction and can also be verbalized into prompt-level cues for LLM-based ERC without modifying backbone models. Experiments on four ERC benchmarks show that AtmosERC improves lightweight ERC, enhances LLM-based ERC as a plug-in cue, and yields more stable predictions under local emotional deviations.
