# Span-Based Fine-Grained Entity-Relation Extraction via Sub-Prompts Combination
## 2 Related Work  
### Joint Entity and Relation Extraction  
The research direction of the joint entity and relation extraction is promising because it can solve the shortcomings of the previous methods, which ignore the interaction between sub-tasks and are affected by error propagation [16; 17; 18; 19; 20].  
Under the guidance of the \(P1\) paradigm, [PERSON] et al. [8] proposed an end-to-end model based on sequence-to-sequence learning with copy mechanism, which can jointly extract relational facts from sentences of any of these classes. Both refs. [9; 10] proposed a span-based joint entity and relation extraction model. The first work showed that with strong negative sampling, span filtering, and a localized context representation, a search over all spans in an input sentence becomes feasible for joint entity and relation extraction. The second work applied MLP attention to capture span-specific features aiming to obtain semantic rich span representation and calculated task-specific contextual representations with attention architecture to further reinforce span and relation representations. Unlike previous work based on BIO labels, these two approaches can identify overlapping entities and inspire us to think about the feasibility of the span-based approach.  
Under the guidance of the \(P2\) paradigm, [PERSON] et al. [11] proposed a multi-turn question answering paradigm for the task of entity-relation extraction and [PERSON] et al. [12] provided an effective solution based on the machine reading comprehension models. These two models cast the entity-relation extraction to a multi-turn question answering (QA) and machine reading comprehension (MRC) task, enlightening us that joint extraction should adapt to more extraction paradigms and approaches.  
Under the guidance of the \(P3\) paradigm, [PERSON] et al. [13] proposed Patti, a novel pattern-first pipeline perspective for entity-relation extraction, which applied a Machine Reading Comprehension (MRC)-based framework to characterize entities and relations better. It alleviated entity redundancy and the entity overlap problem. Moreover, [PERSON] et al. [14] applied a hierarchical reinforcement learning framework to enhance the interaction between entity mentions and relation types. [PERSON] et al. [15] proposed a pipeline approach that first performs sentence classification with relational labels and then extracts the subjects and objects.  
Recently proposed, the seq2 seq model not only performs well in language generation but also in NLU tasks. Instead of tackling the joint extraction by training task-specific discriminative classifiers, [PERSON] et al. [21] framed it as a translation task between augmented natural languages, from which the task-relevant information can be easily extracted. [PERSON] et al. [22] performed relation extraction by representing triples as text sequences.  
After analysis, under the guidance of existing paradigms, there is only a one-step information connection between entities and relations in the extraction process, resulting in insufficient information fusion. Moreover, these models are challenging to adapt to the relation extraction task.