Emotional Supporters often Use Multiple Strategies in a Single Turn
This addresses a gap in emotional support AI for individuals in distress by improving task accuracy, though it is incremental as it refines existing definitions rather than introducing a new paradigm.
The paper tackles the oversimplification in emotional support conversation tasks by redefining them to account for multiple strategies used in a single turn, showing that state-of-the-art large language models outperform supervised models and human supporters under this refined formulation.
Emotional Support Conversations (ESC) are crucial for providing empathy, validation, and actionable guidance to individuals in distress. However, existing definitions of the ESC task oversimplify the structure of supportive responses, typically modelling them as single strategy-utterance pairs. Through a detailed corpus analysis of the ESConv dataset, we identify a common yet previously overlooked phenomenon: emotional supporters often employ multiple strategies consecutively within a single turn. We formally redefine the ESC task to account for this, proposing a revised formulation that requires generating the full sequence of strategy-utterance pairs given a dialogue history. To facilitate this refined task, we introduce several modelling approaches, including supervised deep learning models and large language models. Our experiments show that, under this redefined task, state-of-the-art LLMs outperform both supervised models and human supporters. Notably, contrary to some earlier findings, we observe that LLMs frequently ask questions and provide suggestions, demonstrating more holistic support capabilities.