不同情感理解定义对模型迁移效果影响显著，直接定义更有效。
The Muddy Waters of Modeling Empathy in Language: The Practical Impacts of Theoretical Constructs
- 对比了直接、抽象、邻近三类情感定义的模型迁移表现
- 直接预测特定情感成分的任务迁移性能更高
- 强调需精准、多维地定义情感以提升模型实用性
自然语言处理中情感理解的概念操作化方式多样，有的具明确行为特征，有的较抽象。这些差异如何关联以及是否捕捉文本中可观察的情感属性尚不清晰。为此，我们分析了基于不同理论基础的情感任务模型的迁移性能。研究发现：(1) 情感定义维度存在差异；(2) 定义维度与实际测量属性之间存在对应关系；(3) 数据对表达这些维度的适配性显著影响模型性能，甚至超过其他迁移设置因素的影响。进一步将情感任务的理论基础划分为直接、抽象或邻近三类，结果显示直接预测特定情感成分的任务具有更高迁移性。本研究为精确、多维度的情感操作化提供了实证支持。
原文摘要 · Abstract (English)
Conceptual operationalizations of empathy in NLP are varied, with some having specific behaviors and properties, while others are more abstract. How these variations relate to one another and capture properties of empathy observable in text remains unclear. To provide insight into this, we analyze the transfer performance of empathy models adapted to empathy tasks with different theoretical groundings. We study (1) the dimensionality of empathy definitions, (2) the correspondence between the defined dimensions and measured/observed properties, and (3) the conduciveness of the data to represent them, finding they have a significant impact to performance compared to other transfer setting features. Characterizing the theoretical grounding of empathy tasks as direct, abstract, or adjacent further indicates that tasks that directly predict specified empathy components have higher transferability. Our work provides empirical evidence for the need for precise and multidimensional empathy operationalizations.
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