## 2 Related Works  
### Ere-Related Work  
As the core task of information extraction, entity and relation extraction automatically extracts entities and identifies the relations between entity pairs by modeling textual information, and its research results are primarily applied in automatic question answering, machine translation, knowledge graphs, etc. Early pipeline-based methods [16; 17; 18; 19; 20] for ERE can be divided into two subtasks, called entity recognition (NER) and relation extraction (RE), in chronological order. This approach causes error propagation and ignores the correlation that exists between the two steps. Due to the inherent flaws of the pipeline approaches, researchers have shifted their focus to joint entity and relation extraction. Joint extraction methods integrate ERE and RE, simultaneously extracting entities and classifying the relations of entity pairs. Many joint methods apply parameter sharing or table filling to achieve joint extraction [21; 22; 23; 24], and one of them was proposed by [PERSON] and [PERSON] as the first true neural-network-based joint extraction model [22]. A novel tagging strategy called NovelTagging for assigning tags to words was created by [PERSON] et al. [8], which can transform the joint ERE task into a sequence labeling problem. All of the foregoing works overlooked the fact that an entity may contain multiple relational triples. To assign multiple relations to an entity, the task is considered as a multiple head selection problem [25]. The subject or object of one relational triple can also be the subject or object of another relational triple (SEO), or there may be more than one relation between the entity pairs (EPO) [1]. NovelTagging [8] cannot resolve overlapping relational triples, as a word can only be assigned one tag. A sequence-to-sequence model with a copy mechanism named CopyRE was proposed by [PERSON] et al. [1], in which one entity can be copied several times when it exists in different triples. This method was very novel at that time because it used a fresh perspective to revisit the characteristics of datasets. To consider the interactions between entities and relations, and to overcome the limited number of relational triples generated by CopyRE, a relation extraction model called GraphRel using graph convolutional networks was presented by [PERSON] et al. [9]. [PERSON] et al. [10] found that CopyRE suffers from inaccurate entity copying and the generated entities are not complete, so a multitask learning framework called CopyMTL was introduced to solve these problems. To make up for GraphRel ignoring the relational features, a framework called Relation-Aware improving the relation-aware attention mechanism and building the complete interaction between sentences and relations was designed by [PERSON] et al. [11]. A cascade binary tagging framework called CasRel was proposed by [PERSON] et al. [2], modeling relations as functions that map subjects to objects. It handles the overlapping triple problem by design and provides a general algorithmic framework. TPLinker, a handshaking tagging scheme designed by [PERSON] et al. [12], eliminates the influence of complicated entities in extracting overlapping entity relations by answering questions. In light of the relations implicit in the relation trigger words of the sentence, RIFRE, proposed by [PERSON] et al. [13],takes relations as a priori knowledge in the framework. To reduce computational complexity and avoid sparse labels, [PERSON] et al. [3], based on PRGC (potential relation and global correspondence), decomposed the relational triple extraction into three subtasks: relation judgement, entity extraction, and subject-object alignment. A dual-decoder model was reported by [PERSON] et al. [26], first identifying relations using the text semantics and then considering these relations as additional mapping features to guide entity pair extraction to address the overlapping triple problem.