Before revision: Recent advances in automatic depression detection mostly derive from modality fusion and deep learning methods. However multi-modal approaches insert significant difficulty in datacollection phase while deep learning methods' opaqueness lowers its credibility. This current work proposes a text-based multi-task BLSTM model with pretrained word embeddings .
After revision: 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 BLSTM model with pretrained word embeddings .