发现支持者常一话中用多种策略，重新定义情感对话任务
Emotional Supporters often Use Multiple Strategies in a Single Turn
- 重新定义任务：支持回复需包含多策略连续输出
- 大模型在新任务上表现超越人类支持者
- 适合研究情感计算与对话系统的人关注
情感支持对话（ESC）对提供共情、认可和行动指导至关重要。现有任务定义将支持回应简化为单一策略-话语对，但通过对ESConv数据集的详细语料分析，我们发现支持者常在同一轮对话中连续使用多种策略，这一现象此前未被重视。为此，我们正式重构了ESC任务，要求根据对话历史生成完整的策略-话语序列。为支持这一新任务，我们提出了若干建模方法，包括监督深度学习模型和大语言模型。实验表明，在重构后的任务下，当前最先进的大语言模型表现优于监督模型和人类支持者。值得注意的是，与部分早期研究不同，我们观察到大语言模型频繁提问并提供建议，展现出更全面的支持能力。
原文摘要 · Abstract (English)
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.
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