Affective Conditioning on Hierarchical Attention Networks applied to Depression Detection from Transcribed Clinical Interviews
Table 4: Results of different architectures on the GPC
['[BOLD] Experiment', '[BOLD] Client', '[BOLD] Therapist', '[BOLD] Client+Therapist']
We compare the performance of the proposed models when given as input the client turns (Client), the therapist turns (Therapist) or the whole dialogue (Client+Therapist). We see that the integration of external affective and psycholinguistic features improves model performance for all model configurations over the HAN and SVM baselines. Furthermore, we notice that when we add the summary information we also gain a performance boost, sometimes greater that the external affective information. Summary and lexica integration leads to a performance increase when we provide only the client data. In addition, we see that the client turns are more important for depression detection, as expected, and incorporation of the therapist turns contributes little to the overall model performance. Based on our results, the best performance can be achieved by the HAN+L+S model, while HAN+L model performs best if such annotation is not available.