Conversational Transfer Learning for Emotion Recognition
Table 11: Average performance on ERC with pre-trained weights: {θBERT+θcornellcxt} for VHRED, θcornellcxt contain additional parameters modeling the latent prior state.
['[BOLD] Generative', '[BOLD] IEMOCAP', '[BOLD] Dailydialog']
As a result, our set of transferred parameters contain the additional parameters of MLPθ, included in θsourcecxt. Unfortunately, we do not find significant difference between the parameters from VHRED as opposed to HRED. However, the lack of degradation in the results promise possible future improvements in such designs. We try in-domain tuning of the generative HRED model by performing conversation modeling on the ERC resources. Finally, we transfer these re-tuned weights for the discriminative ERC task. TL between generative tasks, especially with small-scale target resources, is a challenging task. As a result, we find sub-optimal generation in ERC datasets whose further transfer for the classification does not provide any improvement.