The Muddy Waters of Modeling Empathy in Language: The Practical Impacts of Theoretical Constructs

Understand

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.