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 . Our method outputs depression presence results as well as predicted severity score, culminating a state-of-the-art F1 score of 0.87, outperforming previous multi-modal studies. We also achieve the lowest RMSE compared with currently available text-based approaches. Further, by utilizing a per time step attention mechanism we analyse the sentences/words that contribute most in predicting the depressed state. Surprisingly, `unmeaningful' words/paralinguistic information such as `um' and `uh' are the indicators to our model when making a depression prediction. It is for the first time revealed that fillers in a conversation trigger a depression alert for a deep learning model .
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 .
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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
Previous text-based depression detection is commonly based on large user-generated data. Sparse scenarios like clinical conversations are less investigated. This
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BLSTM model
BGRU network
R
. Our
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
R
state-of-the-art
macro
R
0.87, outperforming previous multi-modal studies. We also achieve the lowest RMSE compared with currently available text-based approaches. Further, by utilizing a per time step attention mechanism we analyse the sentences/words that contribute most in predicting the depressed state. Surprisingly, `unmeaningful' words/paralinguistic information such as `um' and `uh' are the indicators to our model when making a depression prediction. It is for the first time revealed that fillers in a conversation trigger a depression alert for a deep learning model
0.84 and MAE of 3.48 on the DAIC-WOZ development set