Observing Dialogue in Therapy: Categorizing and Forecasting Behavioral Codes
Table 12: Ablation study for our proposed model with embeddings trained on the psychotherapy corpus.
['Model C', 'Embedding ELMo', 'macro 53.9', 'Fn  89.6', 'Ct   [BOLD] 39.1', 'St   [BOLD] 33.1', 'macro  [BOLD] 65.4', 'Fa   [BOLD] 95.0', 'Res   [BOLD] 55.7', 'Rec   [BOLD] 54.9', 'Gi   [BOLD] 74.2', 'Quc   [BOLD] 74.8', 'Quo   [BOLD] 82.6', 'Mia   [BOLD] 56.6', 'Min   [BOLD] 29.7']
Domain Specific Glove and ELMo We use the general psychotherapy corpus with 6.5M words (Alexander Street Press) to train the domain specific word embeddings Glovepsyc with 50, 100, 300 dimension. Also, we trained ELMo with 1 highway connection and 256-dimensional output size to get ELMopsyc. We found that ELMo 5.5B performs better than ELMo psyc in our experiments, and general Glove-300 is better than the Glovepsyc. Hence for main results of our models, we use ELMogeneric by default.