对话中情感转录：通过自然语言捕捉细微和复杂情绪状态的基准测试
Emotion Transcription in Conversation: A Benchmark for Capturing Subtle and Complex Emotional States through Natural Language
AI总结：
本文提出对话中情感转录任务，通过自然语言描述捕捉细微复杂情绪，构建了包含日本对话数据集，并评估了基线模型性能。
摘要：
对话中情感识别（ERC）对于实现自然的人机交互至关重要。然而，现有方法大多采用类别或维度情绪标注，往往无法充分表示复杂、细微或文化特定的情绪细微差别。为克服这一限制，我们提出了一项新任务，称为对话中情感转录（ETC）。该任务旨在生成自然语言描述，准确反映说话者在对话情境中的情绪状态。为解决ETC问题，我们构建了一个包含基于文本的对话的日本数据集，这些对话被标注为参与者自报的情绪状态，用自然语言描述。该数据集还包含每个转录的情感类别标签，使定量分析及其应用于ERC成为可能。我们基准测试了基线模型，发现虽然在我们的数据集上微调可以提高模型性能，但当前模型仍难以推断隐含的情绪状态。ETC任务将鼓励进一步研究更丰富的对话中情绪理解。该数据集可在https://github.com/UEC-InabaLab/ETCDataset公开获取。
英文摘要：
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.
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。
