# Domain Adversarial Network for Cross-Domain Emotion Recognition in Conversation
## 4 Experiments
### Experimental Results  
Table 3 presents the results of using different source domains for three target datasets, where \"\(\times\)\" means that only transfer learning is used without the adversarial network [15]; \"\(\backslash\)\" means that the model we proposed uses the adversarial network.  
As can be seen from Table 3, on the three target data, our DAN-CDERC has achieved a significant performance improvement compared with the method without the adversarial network, which is higher by around 5%, 2%, and 7%, respectively. It verifies the effectiveness of our DAN-CDERC model, which can build a good bridge between the  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Parameters** & **IEMOCAP** & **MELD** & **DailyDialog** \\ \hline Embedding Size & 300 & 300 & 300 \\ Utterance Encoder Hidden Size & 768 & 768 & 768 \\ Context Encoder Hidden Size & 256 & 256 & 256 \\ Dropout & 0.1–0.5 & 0.1–0.5 & 0.1–0.5 \\ Learning Rate & 0.0001 & 0.0001 & 0.0001 \\ Batch Size & 4 & 16 & 16 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Setting of hyper-parameters.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Source\(\rightarrow\)Target**} & \multicolumn{2}{c}{**Adversarial Network**} \\ \cline{2-3}  & \(\times\) & \(\surd\) \\ \hline C\(\rightarrow\)I & 59.25 & 64.40 \\ U\(\rightarrow\)I & 58.71 & 63.94 \\ C\(\rightarrow\)M & 57.89 & 59.44 \\ U\(\rightarrow\)M & 57.51 & 59.23 \\ C\(\rightarrow\)D & 48.00 & 55.20 \\ U\(\rightarrow\)D & 47.10 & 54.60 \\ \hline \hline \end{tabular}
\end{table}
Table 3: F1-score of transfer between different domains. Weighted F1 metrics are used to evaluate classification performance.  
source domain and the target domain. Moreover, it demonstrates the importance of domain adaptation in the transfer between different domains for ERC.  
For different source domains, our method shows a primary trend for the three target datasets, where Cornell as the source domain is better than Ubuntu as the source domain, which is higher by 0.46%, 0.21%, and 0.6%, respectively. For transfer learning, in general, the larger amount of source dataset, the better the experimental performance of the target dataset. As shown in Table 1, it is clear that the scale of Ubuntu is an order of magnitude larger than Cornell. To explore this reason, we analyze the characteristics of these datasets. Cornell is composed of movie scripts from multiple websites, and Ubuntu mainly consists of various technical Ubuntu-related problems. For the target domain datasets, the IEMOCAP comes from the drama script, the MELD comes from the movie script, and DailyDialog is the daily dialogue. In terms of content, the similarity between the three target datasets and Cornell is greater than that of Ubuntu. This explains why when Ubuntu is used as the source domain, although the data scale is large, the effect is not as good as Cornell as the source domain dataset.  
It can be observed that our model has apparent effects on the IEMOCAP (5%) and the DailyDialog (7%) compared with the method without the adversarial network. Still, it has little impact on the MELD (2%). This may be due to the fact that the size of IEMOCAP is relatively tiny, and DailyDialog is relatively unbalanced. The knowledge brought by domain migration can compensate for these deficiencies and improve performance. However, MELD is relatively large and balanced, and the transfer of different domains does not contribute much to performance improvement.