论文标题
在低资源场景中利用开放数据和任务增强为心理治疗对话的自动行为编码
Leveraging Open Data and Task Augmentation to Automated Behavioral Coding of Psychotherapy Conversations in Low-Resource Scenarios
论文作者
论文摘要
在心理治疗相互作用中，通过手动观察和注释在对话过程中对参与者的交流行为进行编纂，可以评估会议的质量。开发用于自动行为编码的计算方法可以减轻人类编码人员的负担，并促进对干预的客观评估。但是，在现实世界中，实施此类算法与数据稀疏挑战有关，因为隐私问题导致可用的内域数据有限。在本文中，我们通过通过元学习进行中间语言模型培训来利用一个基于对话的数据集并将知识转移到低资源的行为编码任务中。我们介绍了一种任务增强方法，以产生大量的“类比任务” - 与目标一个类似的任务 - 并证明所提出的框架比所有其他基线模型都更准确地预测目标行为。
In psychotherapy interactions, the quality of a session is assessed by codifying the communicative behaviors of participants during the conversation through manual observation and annotation. Developing computational approaches for automated behavioral coding can reduce the burden on human coders and facilitate the objective evaluation of the intervention. In the real world, however, implementing such algorithms is associated with data sparsity challenges since privacy concerns lead to limited available in-domain data. In this paper, we leverage a publicly available conversation-based dataset and transfer knowledge to the low-resource behavioral coding task by performing an intermediate language model training via meta-learning. We introduce a task augmentation method to produce a large number of "analogy tasks" - tasks similar to the target one - and demonstrate that the proposed framework predicts target behaviors more accurately than all the other baseline models.
