  Previous text-based depression detection is commonly based on large
user-generated data. Sparse scenarios like clinical conversations are less
investigated. This work proposes a text-based multi-task BGRU network with
pretrained word embeddings to model patients' responses during clinical
interviews. Our main approach uses a novel multi-task loss function, aiming at
modeling both depression severity and binary health state. We independently
investigate word- and sentence-level word-embeddings as well as the use of
large-data pretraining for depression detection. To strengthen our findings, we
report mean-averaged results for a multitude of independent runs on sparse
data. First, we show that pretraining is helpful for word-level text-based
depression detection. Second, our results demonstrate that sentence-level
word-embeddings should be mostly preferred over word-level ones. While the
choice of pooling function is less crucial, mean and attention pooling should
be preferred over last-timestep pooling. Our method outputs depression presence
results as well as predicted severity score, culminating a macro F1 score of
0.84 and MAE of 3.48 on the DAIC-WOZ development set.
