## 2 Related Work  
### Domain Adaptation  
Domain adaptation is an active research topic in many fields of artificial intelligence, including machine learning, natural language processing, and computer vision. The methods of early domain adaptation mainly focus on constructing domain-invariant features. This can be achieved by the weight and selection mechanism of features [10; 11] or by learning the explicit feature transformation that aligns the distribution of source domain and target domain [12; 13; 14]. In recent work, deep neural networks have been used to learn powerful representation and perform unsupervised domain adaptation [15]. Revgrad [16] uses a domain classification network, which aims to distinguish between source and target embedding. The goal of the feature extraction network is to produce embedding that maximizes the loss of domain classifiers and minimizes the loss of tag prediction. This is achieved by negating the gradient from the domain classification network. Driven by GAN [17], many transfer learning methods are based on the assumption that a good feature representation contains almost no specific information about the original domain of an instance. For example, [PERSON] et al. proposed a depth structure called Domain-Adversarial Neural Network (DANN) for domain adaptation [16; 18]. Its architecture consists of a feature extractor, tag predictor, and domain classifier. DANN can be trained by inserting a particular gradient reversal layer (GRL). After the training of the whole system, the feature extractor learns the deep features of instances, and the output classifies the prediction tags of unmarked target domain instances. In the field of domain adaptation, some researchers have improved and applied domain adaptation technology. [PERSON] et al. put forward an adaptive model with semantic consistency (ADASC), which gradually and effectively aligns the distinguishing features across domains using the class hierarchy relationship between domains [19]. [PERSON] et al. developed a framework DATSING based on transfer learning, which effectively utilized the potential representation of cross-domain time series to enhance target domain prediction [20]. [PERSON] et al. introduced the task of continuous domain adaptation in machine reading comprehension [21]. There are some other related impressive works. [PERSON] et al.' s work puts forward a unified framework for adaptation in adversarial fields [15]. [PERSON] et al. used Wasserstein distance for domain adaptation [22]. [PERSON] et al. adopted cycle-consistency loss to ensure the consistency of structure and semantics [23]. [PERSON] et al. put forward the condition domain adversarial network, which uses the condition domain discriminator to assist the adversarial adaptation [24]. [PERSON] et al. adopted the symmetrical design of source classifier and target classifier [25]. [PERSON] et al. used a domain confrontation network to solve the problem of multi-source transfer learning [26]. [PERSON] et al. proposed a dynamic adversarial adaptation network [27]. [PERSON] et al. proposed a reinforcement learning (DARL) framework combined with adversarial learning for partial domain adaptation [28]. The DARL framework adopts the deep Q-learning method to learn the strategy by approximating the action value function, so that the agent can make a choice decision. In order to minimize the domain differences in cross domain knowledge transfer, [PERSON] et al. used self similarity consistency (SSC) constraints to match the deep feature structures in the source and target domains, rather than aligning the global distribution statistics across domains [29]. [PERSON] et al. proposed a generative adversary domain adaptive (GADA) algorithm, which significantly improved the process of distinguishing feature extraction by injecting additional classes and using the generated samples for training [30]. [PERSON] et al.