论文标题
在基于文本的咨询中进行自动实时评估
Towards Automated Real-time Evaluation in Text-based Counseling
论文作者
论文摘要
对辅导员 - 客户互动的自动实时评估对于确保质量咨询很重要，但规则很难阐明。机器学习方法的最新进展表明了自动学习此类规则的可能性。但是，这些方法通常需要很难收集的大规模和高质量的咨询数据。为了解决这个问题，我们建立了一个在线咨询平台，该平台使专业的心理治疗师可以为有需要的人提供免费的咨询服务。作为交换，我们收集咨询笔录。在运营后的一年内，我们设法获得了咨询会议的最大（675）个成绩单之一。为了进一步利用我们拥有的有价值的数据，我们使用粗粒和细颗粒标签标记数据集并使用一组训练预处理技术。最后，我们能够在两个标签系统中实现实际有用的准确性。
Automated real-time evaluation of counselor-client interaction is important for ensuring quality counseling but the rules are difficult to articulate. Recent advancements in machine learning methods show the possibility of learning such rules automatically. However, these methods often demand large scale and high quality counseling data, which are difficult to collect. To address this issue, we build an online counseling platform, which allows professional psychotherapists to provide free counseling services to those are in need. In exchange, we collect the counseling transcripts. Within a year of its operation, we manage to get one of the largest set of (675) transcripts of counseling sessions. To further leverage the valuable data we have, we label our dataset using both coarse- and fine-grained labels and use a set of pretraining techniques. In the end, we are able to achieve practically useful accuracy in both labeling system.
