Communication success relies heavily on reading participants' reactions. Such
feedback is especially important for mental health counselors, who must
carefully consider the client's progress and adjust their approach accordingly.
However, previous NLP research on counseling has mainly focused on studying
counselors' intervention strategies rather than their clients' reactions to the
intervention. This work aims to fill this gap by developing a theoretically
grounded annotation framework that encompasses counselors' strategies and
client reaction behaviors. The framework has been tested against a large-scale,
high-quality text-based counseling dataset we collected over the past two years
from an online welfare counseling platform. Our study shows how clients react
to counselors' strategies, how such reactions affect the final counseling
outcomes, and how counselors can adjust their strategies in response to these
reactions. We also demonstrate that this study can help counselors
automatically predict their clients' states.

本研究旨在填补以往咨询研究着重于研究咨询顾问干预策略而非其客户对干预的反应所留下的空白，通过开发一个理论基础的注释框架以及测试所采集的在线福利咨询平台上大规模高质量的基于文本的咨询数据集，研究显示了客户如何对顾问的策略做出反应，这些反应如何影响最终的咨询结果，以及顾问如何针对这些反应调整其策略，该研究还证明了可以帮助咨询顾问自动预测其客户的状态。