Name
Abstract | ||
|---|---|---|
Recent advances in automatic depression detection mostly derive from modality fusion and deep learning methods. However multi-modal approaches insert significant difficulty in data collection phase while deep learning methodsu0027 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, `unmeaningfulu0027 words/paralinguistic information such as `umu0027 and `uhu0027 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. |
Year | Venue | DocType |
|---|---|---|
2019 | arXiv: Learning | Journal |
Volume | Citations | PageRank |
abs/1904.05154 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Heinrich Dinkel | 1 | 23 | 5.79 |
| Mengyue Wu | 2 | 0 | 4.73 |
| Kai Yu | 3 | 1082 | 90.58 |