# Domain Adversarial Network for Cross-Domain Emotion Recognition in Conversation
## 4 Experiments  
### Experimental Settings  
#### 4.1.1 Data  
We choose Cornell Movie Dialog Corpus [46] and Ubuntu Dialog Corpus [47] as the source domain datasets, and call the Cornell Movie Dialog Corpus, Cornell, and the Ubuntu Dialog Corpus, Ubuntu. In all experiments, we carry out IEMOCAP [10], MELD [11] and DailyDialog [48] to evaluate the performance of our model. Table 1 and Figure 4 show the statistics of the datasets.  
* Cornell [46] is a conversational dataset of movie scripts collected from various sites. It contains more than 80,000 dialogues, 300,000 sentences, and 9000 characters in 617 movies.
* Ubuntu [47] is dyadic conversations extracted from the Ubuntu chat logs, which are used to receive technical support for various Ubuntu-related problems. It contains more than one million dialogues and 7 million sentences.
* IEMOCAP [10] is a multimodal dataset, and we only use textual data. Emotion recognition of multimodal data is beyond the scope of this paper. It contains 151 dialogues and 7433 utterances. Each conversation consists of multi-turn utterances, and each  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Dataset** & \multicolumn{2}{c}{**Dialogues**} & **Utterances** \\ \hline \multirow{4}{*}{Source(S)} & \multirow{4}{*}{Cornell(C)} & train & 66,477 & 244,030 \\  & & val & 8310 & 30,436 \\  & & test & 8310 & 30,247 \\ \cline{2-5}  & \multirow{4}{*}{Ubuntu(U)} & train & 898,142 & 6,893,060 \\  & & val & 18,920 & 135,747 \\  & & test & 19,560 & 139,775 \\ \hline \multirow{4}{*}{Target(T)} & \multirow{4}{*}{IEMOCAP(I)} & train/val & 120 & 5810 \\  & & test & 31 & 1623 \\ \cline{1-1} \cline{2-5}  & \multirow{4}{*}{MELD(M)} & train & 1038 & 9989 \\  & & val & 114 & 1109 \\  & & test & 280 & 2610 \\ \cline{1-1} \cline{2-5}  & \multirow{4}{*}{DailyDialog(D)} & train & 11,118 & 87,170 \\  & & val & 1000 & 8069 \\ \cline{1-1}  & & test & 1000 & 7740 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Training, validation and test data distribution in the datasets.  
Figure 4: Distribution of emotions. hap: happy; neu: neutral or no emotion; ang: angry; exc: excited; frus: frustrated; surp: surprise; disg: disgust. (**a**) Distribution of emotions for IEMOCAP. (**b**) Distribution of emotions for MELD. (**c**) Distribution of emotions for DailyDialog.  
utterance is annotated with one of the following emotions: angry, happy, sad, neutral, excited, and frustrated.
* MELD [11] is a multimodal emotion classification dataset that contains textual, acoustic and visual information. MELD is extended from the EmotionLines dataset [49]. It contains 1432 dialogues and 13,708 utterances from the Friends TV series. Each conversation consists of multi-turn utterances, and each utterance is annotated with one of the following emotions: angry, joy, neutral, disgust, sad, surprise and fear.
* DailyDialog [48] is a daily conversation dataset that reflects our daily ways of communication. It contains 13,118 multi-turn dialogues, and the speaker turns are roughly eight. Each utterance has a label of the following emotions: angry, happy, sad, surprise, fear, disgust and neutral (no_emotion).  
As displayed in Figure 4, it can be seen that the distribution of labels is relatively balanced in IEMOCAP. Unlike IEMOCAP and MELD, the no_emotion labels account for 83.1% of DailyDialog. It is unbalanced, so the no_emotion will not be evaluated.