# Domain Adversarial Network for Cross-Domain Emotion Recognition in Conversation
## 5 Analysis and Discussion
### Comparison with Baselines
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{8}{c}{**IEMOCAP**} \\ \cline{2-9}  & **Happy** & **Sad** & **Neutral** & **Angry** & **Excited** & **Frustrated** & **Average (w)** \\ \cline{2-9}  & & & & **F1** & & & **Acc.** & **F1** \\ \hline bc-LSTM & 34.43 & 60.87 & 51.81 & 56.73 & 57.95 & 58.92 & 55.21 & 54.95 \\ CMN & 30.38 & 62.41 & 52.39 & 59.83 & 60.25 & 60.69 & 56.56 & 56.13 \\ ICON & 29.91 & 64.57 & 57.38 & 63.04 & 63.42 & 60.81 & 59.09 & 58.54 \\ DialogueRNN & 33.18 & 78.8 & 59.21 & 65.28 & 71.86 & 58.91 & 63.4 & 62.75 \\ DialogueGCN & 42.75 & 84.54 & 63.54 & 64.19 & 63.08 & 66.99 & 65.25 & 64.18 \\ \hline \(\text{DAN}_{\text{Cornell}}\) & 48.69 & 74.21 & 64.08 & 62.14 & 71.24 & 60.00 & 65.07 & 64.40 \\ \(\text{DAN}_{\text{Ubuntu}}\) & 47.58 & 72.19 & 62.98 & 63.45 & 69.30 & 61.82 & 64.61 & 63.94 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison with different methods on IEMOCAP. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{8}{c}{**MELD**} \\ \cline{2-9}  & **Neutral** & **Surprise** & **Fear** & **Sad** & **Joy** & **Disgust** & **Anger** & **Average (w)** \\ \cline{2-9}  & & & **F1** & & & **Acc.** & **F1** \\ \hline bc-LSTM & 76.23 & 45.10 & 0.00 & 16.11 & 53.17 & 0.00 & 41.06 & 59.43 & 56.44 \\ bc-LSTM+Att & 76.20 & 47.74 & 0.00 & 22.16 & 51.86 & 0.00 & 38.76 & 59.23 & 56.69 \\ DialogueRNN & 75.25 & 49.56 & 3.08 & 22.11 & 52.19 & 0.00 & 42.58 & 58.47 & 57.04 \\ DialogueGCN & - & - & - & - & - & - & - & - & 58.10 \\ \hline \(\text{DAN}_{\text{Cornell}}\) & 77.58 & 54.29 & 0.00 & 25.93 & 57.00 & 0.00 & 40.97 & 57.56 & 59.44 \\ \(\text{DAN}_{\text{Ubuntu}}\) & 77.18 & 52.45 & 0.00 & 29.14 & 57.97 & 0.00 & 39.23 & 57.50 & 59.23 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison with different methods on MELD. As shown in Table 7, we make statistics on the average length of the dialogue, and the average length of the utterance in the IEMOCAP, MELD and DailyDialog. The average dialogue length of the IEMOCAP dataset is around 50 utterances, while the MELD is around 10 and the DailyDialog is around eight. Moreover, the IEMOCAP has five sessions and two participants in each conversation. The MELD has multiple participants, more than 300. Although there are only two participants in each conversation in the DailyDialog dataset, they are collected from dialogues in different scenarios. That is to say, there is no relationship between the conversations. The IEMOCAP, which more easily models inter-dependencies and self-dependencies than the MELD (short conversations and many participants) and DailyDialog (short conversations and a weak correlation between conversations), have long conversations, few participants, and a strong correlation between conversations. This is why our model does not perform as well on the IEMOCAP as on MELD and DailyDialog compared with the other best models. Those models are more conducive to establishing dependencies, while our model lacks this ability. Experiments show that our model is effective on three datasets. In addition, since the proposed model does not model the speakers' information, it is effective not only for dyadic conversations, but also for multi-party conversations.