  Many statistical models have high accuracy on test benchmarks, but are not
explainable, struggle in low-resource scenarios, cannot be reused for multiple
tasks, and cannot easily integrate domain expertise. These factors limit their
use, particularly in settings such as mental health, where it is difficult to
annotate datasets and model outputs have significant impact. We introduce a
micromodel architecture to address these challenges. Our approach allows
researchers to build interpretable representations that embed domain knowledge
and provide explanations throughout the model's decision process. We
demonstrate the idea on multiple mental health tasks: depression
classification, PTSD classification, and suicidal risk assessment. Our systems
consistently produce strong results, even in low-resource scenarios, and are
more interpretable than alternative methods.
