## 2 Related Work  
Relation extraction is an important research task in the NLP area. Many works regard RE as a supervised classification task and have achieved good results. One of the main defects of these conventional supervised relation extraction methods is the lack of manually annotated training data, which is expensive and time-consuming. To deal with this issue, distant supervision was proposed by [PERSON] et al. [3] to generate a large amount of training data automatically by aligning text to corresponding KGs. At present, many DSRE works are combined with a neural network model to achieve good results. [PERSON] et al. [4] and [PERSON] et al. [5] employed a recursive neural network (RNN) and a convolutional neural network (CNN) to obtain text representation, respectively. [PERSON] et al. [6] attempted to propose a piecewise convolution neural network for RE. They considered the relative position information of each word and entities and adopted piecewise pooling on entity position to retain more fine-grained information in the sentence. In addition, the addition of additional knowledge associated with text can further improve DSRE and become one of the new research directions. [PERSON] et al. [7] proposed a sentence-level attention model that made full use of extracted entity descriptions to provide more background knowledge. [PERSON] et al. [8] employed graph convolution networks to capture syntactic information from text and utilized available side information such as entity type and relation alias to improve RE. [PERSON] et al. [9] combined distant supervision with a directly supervised data and used it to improve weights of relevant sentences. [PERSON] et al. [10] proposed a hierarchical framework to fuse information from DS and web tables that share relational facts about entities to further improve RE. The above methods have achieved good results, but because of the strong assumption of DS, the data generated inevitably contains sentences that are mistaken for their strong assumption. Moreover, adding two-hop DS data may bring new noise. Therefore, how to fully join the existing entity data and additional knowledge and get rid of the noise problem is an urgent problem for RE. The multi-instance learning (MIL) framework is an important method of noise reduction in DSRE. [PERSON] et al. [11] introduced a multi-instance single-label learning framework to RE and assumed that the positive instance must exist and the instance with the highest confidence can express the label. [PERSON] et al. [12] and [PERSON] et al. [13] adopted multi-instance multi-label learning to model multiple relations between entities. In recent years, to alleviate the noise problem, many RE studies based on the MIL framework have employed neural networks with a selective attention mechanism to assign weights to different instances within the bag. [PERSON] et al. [14] proposed a CNN-based model with a sentence-level attention mechanism to assign more weights to effective instances and reduce the weights of noisy instances. [PERSON] et al. [15] introduced a soft-label method to exploit valid information from correctly labeled entity pairs to help noisy instances. [PERSON] et al. [16] proposed a - word attention model based on the Bidirectional Gated Recurrent Unit (BiGRU) and an entity-centric attention model to identify key words in sentences. [PERSON] et al. [17] proposed a hybrid attention-based Transformer block to get word-level features and constitute the bag representation. [PERSON] et al. [18] presented a reinforcement-learning-based framework to choose positive instances and make full use of unlabeled instances. Although the above methods have achieved good results, they still use noisy sentences in training and do not consider the problem of bag-level noise labeling, so do not fundamentally get rid of the noise problems.