# Domain Adversarial Network for Cross-Domain Emotion Recognition in Conversation
## 5 Analysis and Discussion
### Comparison with Baselines  
We compare our model with various baseline approaches for emotion recognition in conversation. * **bc-LSTM** is a basic model which employs BiLSTM to capture contextual content from the surrounding utterances without distinguishing different speakers;
* **CMN**[51] is the Conversational Memory Network, which models utterance context from dialogue history using two GRUs for speakers. Then, utterance representation is obtained by feeding the current utterance as the query to two memory networks for different speakers;
* **ICON**[6] uses GRU to model the self and inter-speaker sentiment influences and employs a memory network to store contextual summaries for classification. In our implementation, we only use the uni-modal classification;
* **DialogueRNN**[3] employs three GRUs (global GRU, party GRU, and speaker GRU) to model the speaker, the context and the emotion of the preceding utterances;
* **DialogueGCN**[4] uses a graph convolutional neural network to model self and inter-speaker dependencies. It represents each utterance as a node and models the dependencies between the speakers of those utterances by leveraging the edges between a pair of nodes/utterances. * **DAN\({}_{\text{Cornell}}\)** means that the source domain is Cornell. * **DAN\({}_{\text{Ubuntu}}\)** means that the source domain is Ubuntu. **IEMOCAP:** Table 4 presents the results of our proposed DAN-CDERC model and strong baselines. DAN\({}_{\text{Cornell}}\) and DAN\({}_{\text{Ubuntu}}\) achieve an average F1-score of 64.40%, 63.94% and an accuracy of 65.07% and 64.61%, respectively. To our surprise, the F1-score of DAN\({}_{\text{Cornell}}\) outperforms DialogueGCN (when the learning rate is 0.000085, DAN\({}_{\text{Cornell}}\) achieves an F1-score of 64.68%, which is a 0.5% improvement over DialogueGCN). Although DAN\({}_{\text{Ubuntu}}\) does not perform as well as DialogueGCN, the difference is small, and it improves 1.19% over DialogueRNN. This is because, as mentioned in Section 4.2 above, the similarity between IEMOCAP and Cornell is small, and we mainly use adversarialnetworks to reduce the difference between source and target domains, without resorting to complex modeling inter and self-party dependency. For individual labels, our method also achieves a good performance. The MELD is a multi-party conversations dataset, and there are more than 300 speakers in the dataset. Normally, there are several participants in each conversation, for example, in Figure 1 where there are five participants. Additionally, we also observe that many speakers in a conversation do not utter alternately, but one speaker may utter several utterances continuously. Hence, it is not easy for models such as DialogueGCN to model the speaker's information successfully. **DailyDialog:** Table 6 presents the results of DialogueRNN, DialogueGCN, \(\text{DAN}_{\text{Cornell}}\) and \(\text{DAN}_{\text{Ubuntu}}\) (since the DailyDialog is seriously unbalanced, we add two additional evaluation metrics, Micro F1 and Macro F1). Table 6 clearly shows that DialogueGCN performs poorly. Performance improvement is difficult due to the imbalance of the DailyDialog dataset compared with DialogueRNN, but \(\text{DAN}_{\text{Cornell}}\) and \(\text{DAN}_{\text{Ubuntu}}\) still achieve a 1.42% and 0.82% improvement on Weighted F1-score, respectively. Besides, our proposed DAN-CDERC model outperforms baseline models in terms of Micro F1 and Macro F1. To explain this gap in performance, it is essential to understand the distribution of emotions for DailyDialog. From Figure 4, most of the utterances are emotionless, and it may not be possible to model the speaker's information successfully by using DialogueRNN and DialogueGCN, compared with IEMOCAP.