The Muddy Waters of Modeling Empathy in Language: The Practical Impacts of Theoretical Constructs
This work addresses the challenge of inconsistent empathy modeling in NLP for researchers and practitioners, though it is incremental in providing empirical evidence for existing theoretical concerns.
The paper tackled the problem of varied conceptual operationalizations of empathy in NLP by analyzing transfer performance of models across tasks with different theoretical groundings, finding that tasks directly predicting specified empathy components have higher transferability.
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.