Real-Time Emotion Recognition via Attention Gated Hierarchical Memory Network
Table 3: Performance of AGHMN models on MELD.
['Model', '[BOLD] MELD: Emotion Classes  [ITALIC] neutral', '[BOLD] MELD: Emotion Classes  [ITALIC] neutral', '[BOLD] MELD: Emotion Classes  [ITALIC] surprise', '[BOLD] MELD: Emotion Classes  [ITALIC] surprise', '[BOLD] MELD: Emotion Classes  [ITALIC] fear', '[BOLD] MELD: Emotion Classes  [ITALIC] fear', '[BOLD] MELD: Emotion Classes  [ITALIC] sadness', '[BOLD] MELD: Emotion Classes  [ITALIC] sadness', '[BOLD] MELD: Emotion Classes  [ITALIC] joy', '[BOLD] MELD: Emotion Classes  [ITALIC] joy', '[BOLD] MELD: Emotion Classes  [ITALIC] disgust', '[BOLD] MELD: Emotion Classes  [ITALIC] disgust', '[BOLD] MELD: Emotion Classes  [ITALIC] anger', '[BOLD] MELD: Emotion Classes  [ITALIC] anger', '[BOLD] MELD: Emotion Classes  [BOLD] Avg.', '[BOLD] MELD: Emotion Classes  [BOLD] Avg.', '[BOLD] MELD: Emotion Classes  [BOLD] Avg.']
For both datasets, we report the accuracy and F1-score We also report the macro-average of F1-score (mF1) to reflect the model performance on minority emotion classes, since the weighted-average is compromised by the majority classes. Each result provided by us in the tables is the average value of 10 times repeated experiments. But this time, UniF-BiAGRU becomes the best one (+2.8% Acc, +2.2% F1, +3.3% mF1). Our models perform the best on most emotion classes, especially the two minority classes fear and disgust, though this is accompanied by the performance degradation on anger. But referring to the mF1 value, it is safe to say that our models produce much more balanced results. Baseline Methods. The bcLSTM model implemented by us is very strong, performing better than all the other baselines on both datasets except DialogueRNN△ on IEMOCAP. However, the results of DialogueRNN run by us are slightly worse than DialogueRNN△. This may be because we follow the default settings of the provided codes, which are customized for BiDialogueRNN. No matter what, our AGHMN models outperform them on the two datasets, suggesting the efficacy of our context-modeling scheme. AGHMN variants. UniF-AGRU performs the worst among all the four variants on both datasets. Consistently, UniF-BiAGRU and BiF-AGRU outperform UniF-AGRU, which demonstrates the superiority of BiAGRU over AGRU and BiF over UniF, respectively. However, BiF-BiAGRU does not attain the best performance, which we speculate that the model becomes too deep to learn from the two datasets. In fact, the performance difference between the variants on MELD is limited. This is mainly because the conversations in MELD contain much fewer turns than that in IEMOCAP, making it less sensitive to different modules.