There has been considerable research in the field of automated mental health analysis. Studies based on patient-therapist interviews usually treat the dyadic discourse as a sequence of sentences, thus ignoring individual sentence types (question or answer). To avoid this situation, we design a multi-view architecture that retains the symmetric discourse structure by dividing the transcripts into patient and therapist views. Experiments on the DAIC-WOZ dataset for depression level rating show performance improvements over baselines and state-of-the-art models.