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Recent works have demonstrated ability to assess aspects of mental healthfrom personal discourse. At the same time, pre-trained contextual wordembedding models have grown to dominate much of NLP but little is knownempirically on how to best apply them for mental health assessment. Usingdegree of depression as a case study, we do an empirical analysis on whichoff-the-shelf language model, individual layers, and combinations of layersseem most promising when applied to human-level NLP tasks. Notably, we findRoBERTa most effective and, despite the standard in past work suggesting thesecond-to-last or concatenation of the last 4 layers, we find layer 19(sixth-to last) is at least as good as layer 23 when using 1 layer. Further,when using multiple layers, distributing them across the second half (i.e.Layers 12+), rather than last 4, of the 24 layers yielded the most accurateresults.

Evaluating Contextual Embeddings and their Extraction Layers for Depression Assessment