发表机构
Cornell University; Crisis Text Line（康奈尔大学; 危机文本线）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对心理健康危机志愿者咨询师，提出预测其长期对话技能发展的任务，通过识别其对话困难时刻及后续调整方式，构建的咨询师适应方法比基线方法更优。
AI 中文摘要
人们如何学习成为更好的对话者？这一问题在心理健康咨询领域尤为重要，因为对话技能是核心，但志愿者咨询师往往难以获得督导和结构化反馈。理解咨询师如何发展引导对话走向积极结果的能力，并及早识别哪些咨询师（未）处于进步轨道，有助于优先为最需要支持的咨询师提供帮助。在本研究中，我们引入了一项任务：在对话者职业生涯早期预测其最终是否会在引导对话走向积极结果方面有所进步，并以心理健康危机志愿者咨询师为例证明了该任务的可行性。我们的核心见解是，人们可能会在对话中的特定类型时刻遇到困难，而最能揭示其未来进步可能性的是他们如何随着时间的推移学习处理这些时刻。我们将这一见解落地为一种方法：识别咨询师最初遇到困难的时刻类型，捕捉他们在后续对话中再次遇到类似时刻时如何调整回应，并学习哪些早期调整能预测数月甚至数年后的进步。尽管这项未来预测任务具有挑战性，但我们的咨询师适应方法比直接从对话文本学习的基线方法取得了更好的结果。
英文摘要
How do people learn to become better conversationalists? This question is especially important in the context of mental-health counseling, where conversational skills are essential, yet volunteer counselors often have limited access to supervision and structured feedback. Understanding how counselors develop their ability to steer conversations toward positive outcomes -- and identifying early which counselors are (not) on track to improve -- can help prioritize support for the counselors who need it most. In this work, we introduce the task of predicting, early in a conversationalist's career, whether they will eventually improve at steering conversations toward positive outcomes, and demonstrate the feasibility of this task in the case of volunteer mental-health crisis counselors. Our central insight is that people may struggle with particular kinds of moments in a conversation, and that what is especially revealing of their likelihood of future improvement is how they learn to handle those moments over time. We operationalize this insight by designing a method that identifies the types of moments a counselor initially struggles with, captures how they adapt their response when they re-encounter similar moments in subsequent conversations, and learns which early adaptations predict improvement months or even years later. While this future-prediction task is challenging, our counselor-adaptation approach yields better results than baselines that learn directly from the conversation transcript.
CommentsTo be presented at EMNLP 2026. Code available at convokit.cornell.edu
