DiaRelay：使用固定大小内存传递对话上下文以实现对话中的情感识别
DiaRelay: Relaying Dialogue Context with a Constant-Size Memory for Emotion Recognition in Conversation
浏览论文内容
中文总结 AI 辅助
本文提出基于LoRA的轻量适配器DiaRelay，通过新增两个内存组件解决现有ERC方法的局限，在MELD、IEMOCAP数据集上取得良好效果，验证了其有效性与通用性。
中文摘要 AI 辅助
对话中的情感识别（ERC）要求模型识别通常分布在对话不同轮次中的细微情感线索。现有方法通常通过固定上下文窗口整合对话历史，但短窗口会丢弃潜在有用的长程证据，而扩大窗口会重复对重叠话语进行编码，增加计算和内存成本，还可能引入无关上下文。此外，常用的参数高效适配方法（如LoRA）主要在特征空间引入固定低秩变换，未显式维护对话级状态或根据不断变化的对话上下文调整变换。为解决这些局限，本文提出轻量适配器DiaRelay，使大语言模型（LLM）能显式维护对话级内存以实现准确的ERC。DiaRelay基于LoRA，新增两个紧密协作的组件：选择性传递内存转换（Selective Relay Memory Transition）和双轴传递内存读取（Dual-axis Relay Memory Read）。选择性传递内存转换会逐步将有用的历史证据聚合到有界传递内存中，并在后续话语预测中传播，使较早的情感线索在离开局部上下文窗口后仍能影响后续预测，无需重新编码完整对话历史或扩大主干上下文长度；双轴传递内存读取利用传播的内存动态调节低秩特征变换，实现依赖上下文的表示适配，无需测试时梯度更新。大量实验表明，DiaRelay在MELD数据集上可达到SOTA加权F1值和准确率，在IEMOCAP数据集上也取得有竞争力的结果，且仅需额外710万可训练参数，证明了DiaRelay在增强基于LLM的情感理解方面的有效性和通用性。
英文摘要
Emotion Recognition in Conversation (ERC) requires models to identify subtle emotional cues that are often distributed across distant dialogue turns. Existing methods typically incorporate dialogue history through a fixed context window. However, short windows discard potentially useful long-range evidence, while enlarging the window repeatedly re-encodes overlapping utterances, increases computational and memory costs, and may introduce irrelevant context. Moreover, commonly used parameter-efficient adaptation methods, such as LoRA, mainly introduce fixed low-rank transformations in the feature space and do not explicitly maintain a dialogue-level state or condition their transformations on the evolving conversational context. To address these limitations, we propose a lightweight adapter, DiaRelay, to enable LLMs to explicitly maintain a dialogue-level memory for accurate ERC. Based on LoRA, DiaRelay introduces two extra tightly collaborative components, Selective Relay Memory Transition and Dual-axis Relay Memory Read. Selective Relay Memory Transition progressively aggregates useful historical evidence into a bounded relay memory and propagates it across successive utterance predictions. This allows earlier emotional cues to influence later predictions after they leave the local context window, without re-encoding the complete dialogue history or expanding the backbone context length. Dual-axis Relay Memory Read uses the propagated memory to dynamically modulate low-rank feature transformations, enabling context-dependent representation adaptation without test-time gradient updates. Extensive experiments show that DiaRelay can achieve SOTA weighted F1 and accuracy on MELD while obtaining competitive results on IEMOCAP with only an extra 7.1M trainable parameters, indicating the effectiveness and generalizability of our DiaRelay in enhancing LLM-based emotional understanding.
