Conversational Transfer Learning for Emotion Recognition
Table 9: Average performance on ERC with pre-trained weights: {θBERT+θcornellcxt}. Note: DD here means DailyDialog.
['[EMPTY]', '[BOLD] Iemocap', '[BOLD] SEMAINE DV', '[BOLD] SEMAINE DA', '[BOLD] SEMAINE DP', '[BOLD] SEMAINE DE']
As seen, our proposed TL-ERC comfortably outperforms both non-contextual and contextual baselines. It achieves this without the aid of attention mechanisms that is used in c-LSTM + Att, multi-hop memory networks used in Memnet, and CMN. It also achieves competitive performance against DialogueRNN, which has three layers of inter-utterance recurrent layers, while TL-ERC has one. These trends indicate TL to be effective in our setup and provided promising directions for future research. We try two primary adaptation techniques used in inductive TL, freezed or fine-tuned. In the former setting, the borrowed weights are used for feature extraction, while in the latter, we train the weights along with the other new parameters of the target task’s model. Fine-tuning can also be performed using other techniques such as gradual unfreezing Peters et al. We notice a degradation in performance with more frozen parameters. The datasets in ERC contain multi-class annotations with varying label distributions. With frozen parameters, our transferred model is unable to account for the label distribution and results in low recall for infrequent classes. We thus find the fine-tuning approach to be more effective in this setup.