用自然语言描述对话中的微妙情绪，提升人机交互的情感理解能力。
Emotion Transcription in Conversation: A Benchmark for Capturing Subtle and Complex Emotional States through Natural Language
- 提出对话情绪转录新任务，用自然语言描述情绪状态。
- 构建日语对话数据集，含自述情绪描述与类别标签。
- 现有模型仍难捕捉隐含情绪，适合情感计算研究者使用。
对话中的情绪识别（ERC）对实现自然的人机交互至关重要。然而，现有方法多采用分类或维度化情绪标注，难以充分表达复杂、微妙或文化特异的情绪细节。为此，我们提出一种新任务——对话情绪转录（ETC），聚焦于生成准确反映对话情境中说话人情绪状态的自然语言描述。为支持该任务，我们构建了一个基于文本的日语对话数据集，包含参与者自述的情绪状态描述，并附有情绪类别标签，便于量化分析和应用于ERC。我们对基线模型进行了基准测试，发现尽管在本数据集上微调能提升性能，但当前模型仍难以推断隐含情绪状态。该任务将推动对话中更丰富的情绪理解研究。数据集已公开于 https://github.com/UEC-InabaLab/ETCDataset。
原文摘要 · Abstract (English)
Emotion Recognition in Conversation (ERC) is critical for enabling natural human-machine interactions. However, existing methods predominantly employ categorical or dimensional emotion annotations, which often fail to adequately represent complex, subtle, or culturally specific emotional nuances. To overcome this limitation, we propose a novel task named Emotion Transcription in Conversation (ETC). This task focuses on generating natural language descriptions that accurately reflect speakers' emotional states within conversational contexts. To address the ETC, we constructed a Japanese dataset comprising text-based dialogues annotated with participants' self-reported emotional states, described in natural language. The dataset also includes emotion category labels for each transcription, enabling quantitative analysis and its application to ERC. We benchmarked baseline models, finding that while fine-tuning on our dataset enhances model performance, current models still struggle to infer implicit emotional states. The ETC task will encourage further research into more expressive emotion understanding in dialogue. The dataset is publicly available at https://github.com/UEC-InabaLab/ETCDataset.
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