Affective Conditioning on Hierarchical Attention Networks applied to Depression Detection from Transcribed Clinical Interviews
Table 5: Results of the DAIC-WoZ corpus development set
['[BOLD] Method', '[BOLD] F1-macro', '[BOLD] UAR']
We observe that affective conditioning significantly improves the performance over the baseline model (HAN). Our HAN+L model also leads to improved F1 and AUR scores over the models proposed in [same]. Overall, we see that conditioning of external psycholinguistic knowledge in this small dataset (189 samples) enhances the performance and the results are comparable to these of the GPC dataset.