  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.
