# SAPBERT: Speaker-Aware Pretrained BERT for Emotion Recognition in Conversation
## 6 Results
### Experimental Results  
As shown in Table 4, our model shows the best performance in just two emotions: happy and neutral, but with the best score of both accuracy and f1-score on average. This may indicate that our proposed model is not specialized in capturing only specific emotions, but overall emotions evenly well.  
Unlike other models, our model is trained to distinguish the speakers in a dialogue. Therefore, our model may be able to capture both speaker-dependent contexts and the context of emotion such as inter-speaker dependency or self-dependency better than other baselines.  
TL-ERC [11] shows the efficiency of transfer learning from the generative task to the ERC task. As shown in Table 5, our model achieves approximately 8% better in F-1 score than TL-ERC with the IEMOCAP dataset. This demonstrates the effectiveness of the multi-task learning.  
As shown in Tables 4 and 5, our model achieves approximately 8%, 2% and 0.2% better than each commonsense knowledge based models, KET [4], COSMIC [5], KI-Net [6], on the IEMOCAP dataset. This indicates that capturing speaker-dependent context can be important.  
For the MELD dataset, COSMIC [5] shows a little better performance. This may be attributed to the structural characteristics of MELD conversation. The number of speakers in each conversation is large (up to 9) and the average length of conversations is 10 in the MELD dataset, while in IEMOCAP the average length of conversations is 50 and the number of speakers is 2. This means that MELD has very few utterances per speaker in each conversation for our model to capture speaker-dependent contexts.  
Our model gains approximately 2.5% F-1 score improvement on the IEMOCAP dataset over BiERU [25], as shown in Table 4. This indicates the importance of capturing the speaker-dependent contexts. Approximately a 1% improvement in the F-1 score on the IEMOCAP dataset over DialogXL [20] and DialogueCRN [19] demonstrates that Speaker Classification can be effective for capturing the speaker-dependent contexts.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**IEMOCAP**} & \multicolumn{1}{c}{**MELD**} \\
**Models** & **Avg (w) Acc.** & **Avg (w) F1.** & **Avg (w) F1.** \\ \hline TL-ERC & - & 59.30 & - \\ KET & - & 59.56 & 58.18 \\ COSMIC & - & 65.28 & **65.21** \\ DialogXL & - & 65.94 & 62.41 \\ DialogueCRN & 66.05 & 66.20 & 58.39 \\ CESTa & - & 67.10 & 58.36 \\ \hline Our Model & **67.34** & **67.16** & 64.28 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparisons with baselines and our method. Avg(w) refers to weighted average, Acc refers to the accuracy and F1 refers to the f1-score. Best performances are highlighted in bold.  
As shown in Table 5, our model achieves approximately 6% better on the MELD dataset and 0.1% better on the IEMOCAP dataset compared to the CESTa [18]. This can be attributed to the capturing of the speaker-dependent contexts during a dialogue.  
As shown in Table 6, the performance of our model without the pre-training of Speaker Classification is lower than some of the baseline models such as DialogueGCN [3], AGHMN [24], BiERU [25], KI-Net [6], COSMIC [5], DialogXL [20], DialogueCRN [19] and CESTa [18]. This indicates that our model's improvement may not be due to the difference between the model structures.