# Domain Adversarial Network for Cross-Domain Emotion Recognition in Conversation
## 6 Conclusions  
Given the lack of large-scale publicly available datasets, transfer learning is an effective way to alleviate this problem. We present a _Domain Adversarial Network for Cross-Domain Emotion Recognition in Conversation_ (DAN-CDERC) model, consisting of two parts, namely the domain adversarial model and the emotion recognition model. The domain adversarial network employs a conversational dataset to train the generative task and a source domain and target domain dataset to train the domain discriminator for domain adaptation simultaneously. The emotion recognition model receives the transferred sequence knowledge and recognizes the emotions. When Cornell is the source dataset, the DAN-CDERC achieves an F1 of 64.40%, 59.44% and 55.20% on three datasets, all outperforming the baselines, without resorting to complex modeling inter and self-party dependency. In addition, the data scale of the source domain will have an impact on emotion recognition, but for different source domain datasets, domain similarity is more important than the data scale. Since DAN-CDERC does not model speakers' information, it is effective not only for dyadic conversations, but also for multi-party conversations. Our method proves the feasibility of using conversational datasets and domain adaptation for ERC. Although this paper attempts to solve the problem of domain adaptation for ERC, there is inconsistency in the domain space between different tasks and different datasets, which has not been fully considered. In addition, due to the introduction of large-scale data and the use of adversarial networks in this paper, the training time of the model on the source task is long, which is also one of the shortcomings of adversarial transfer networks. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Turn** & **Utterance** & **Emotion** & **DialogueGCN** & **DAN\({}_{\text{Cemel}}\)** \\ \hline \(\mathrm{T}_{\mathrm{F}}^{\mathrm{45}}\) & What the hell is this? & ang & fru & ang \\ \(\mathrm{T}_{\mathrm{M}}^{\mathrm{46}}\) & I’ll get out. I’ll get married and live some place else. Maybe, maybe New York? & fru & nap & ang \\ \(\mathrm{T}_{\mathrm{F}}^{\mathrm{47}}\) & Are you crazy? & ang & fru & ang \\ \(\mathrm{T}_{\mathrm{M}}^{\mathrm{48}}\) & Wait a minute. Tell me this. Do you mean to say that you would leave the business? & fru & fru & fru \\ \(\mathrm{T}_{\mathrm{F}}^{\mathrm{49}}\) & The business? The business? It doesn’t inspire me? & ang & fru & ang \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Our method is compared with DialogueGCN for an example from IEMOCAP. In the future, using more and faster adaptation strategies to solve the task of ERC is worthy of continuous and in-depth research. Conceptualization, H.M. and X.Z. ; methodology, H.M. and X.Z. ; software and experiments, H.M.; resources, Q.Z. ; writing--original draft preparation, H.M.; draft revision, H.M., C.Z., X.Z., J.C. and Q.Z. ; supervision, Q.Z. ; project administration, Q.Z. ; funding acquisition, X.Z. and Q.Z. All authors read and agreed to the published version of the manuscript. This work was funded by National Natural Science Foundation of China Grant No. 61972133 and National Natural Science Foundation of China Grant No. 62176174. Not applicable. The IEMOCAP dataset used in this study can be found at [[https://sail.usc.edu/iemocap/iemocap_release.htm](https://sail.usc.edu/iemocap/iemocap_release.htm)]([https://sail.usc.edu/iemocap/iemocap_release.htm](https://sail.usc.edu/iemocap/iemocap_release.htm)), accessed on 12 March 2021. The MELD dataset used in this study can be found at [[https://affective-meld.github.io/](https://affective-meld.github.io/)]([https://affective-meld.github.io/](https://affective-meld.github.io/)), accessed on 12 March 2021. The DailyDialog dataset used in this study can be found at [[http://yanran.li/dailydialog](http://yanran.li/dailydialog)]([http://yanran.li/dailydialog](http://yanran.li/dailydialog)), accessed on 12 March 2021.