Leveraging Open Data and Task Augmentation to Automated Behavioral Coding of Psychotherapy Conversations in Low-Resource Scenarios
This work addresses data sparsity challenges in psychotherapy evaluation, offering a computational solution to reduce manual coding burden, though it is incremental in applying meta-learning and augmentation techniques.
The paper tackled the problem of automated behavioral coding in psychotherapy conversations under low-resource conditions by leveraging open data and task augmentation, resulting in a framework that predicts target behaviors more accurately than baseline models.
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.