# Domain Adversarial Network for Cross-Domain Emotion Recognition in Conversation
###### Abstract  
Emotion Recognition in Conversation (ERC) aims to recognize the emotion for each utterance in a conversation automatically. Due to the difficulty of collecting and labeling, this task lacks the dataset corpora available on a large scale. This increases the difficulty of finishing the supervised training required by large-scale neural networks. Introducing the large-scale generative conversational dataset can assist with modeling dialogue. However, the spatial distribution of feature vectors in the source and target domains is inconsistent after introducing the external dataset. To alleviate the problem, we propose a Domain Adversarial Network for Cross-Domain Emotion Recognition in Conversation (DAN-CDERC) model, consisting of domain adversarial and emotion recognition models. The domain adversarial model consists of the encoders, a generator and a domain discriminator. First, the encoders and generator learn contextual features from a large-scale source dataset. The discriminator performs domain adaptation by discriminating the domain to make the feature space of the source and target domain consistent, so as to obtain domain invariant features. Then DAN-CDERC transfers the learned domain invariant dialogue context knowledge from the domain adversarial model to the emotion recognition model to assist in modeling the dialogue context. Due to the use of a domain adversarial network, DAN-CDERC obtains dialogue-level contextual information that is domain invariant, thereby reducing the negative impact of inconsistency in domain space. Empirical studies illustrate that the proposed model outperforms the baseline models on three benchmark emotion recognition datasets. emotion recognition in conversation; domain adversarial network; domain adaptation; transfer learning +
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data IEMOCAP (the IEMOCAP and MELD are two common datasets for ERC, which will be described in detail in Section 4.1) [10], there are only 153 dialogues. It is challenging to finish the supervised training required by large-scale neural networks. In addition, some datasets contain only two participants in each conversation, while others have multiple participants. Figure 1 shows a conversation from the MELD (the IEMOCAP and MELD are two common datasets for ERC, which will be described in detail in Section 4.1) [11], which comprises five participants. It is challenging to model speakers dependencies. The IEMOCAP dataset has five sessions, and the last session is divided into test data, which means that there are different speakers in the training data and the test data. These factors have made it more challenging to model speakers and emotion dependencies. Cross-domain sentiment classification, which aims to transfer knowledge to the target domain from the source domain, is one of the effective ways to alleviate the lack of datasets in the target domain. Many excellent achievements have been made in cross-domain sentiment classification [12; 13; 14]. Cross-domain sentiment classification generally requires the source domain data to be labeled. Unlike sentiment classification, ERC lacks large scale datasets and aims to identify the emotion of each utterance in a conversation, rather than a single sentence. An utterance of the speaker is affected by their own and external factors, such as topic, speaker's personality, argumentation logic, viewpoint, intent, etc. [9]. In turn, the utterance reflects these factors to a certain extent. These factors may lead to improved conversation understanding, including emotion recognition [9]. For the above reasons, aiming at the problem of lack of labeled data, [PERSON] et al. [15] pre-trained a whole conversation jointly using a hierarchical generative model and transfer the knowledge to the target domain. In summary, the generation task can be used to assist the ERC task, since the dialogue generation task and ERC have some similarities. However, the spatial distribution of feature vectors in the source and target domains is inconsistent after introducing external generation datasets.