SRF : Enhancing Document-Level Relation Extraction with a Novel Secondary Reasoning Framework (2024)

Abstract: Document-level Relation Extraction (DocRE) aims to extract relations between entity pairs in a document and poses many challenges as it involves multiple mentions of entities and cross-sentence inference. However, several aspects that are important for DocRE have not been considered and explored. Existing work ignores bidirectional mention interaction when generating relational features for entity pairs. Also, sophisticated neural networks are typically designed for cross-sentence evidence extraction to further enhance DocRE. More interestingly, we reveal a noteworthy finding: If a model has predicted a relation between an entity and other entities, this relation information may help infer and predict more relations between the entity’s adjacent entities and these other entities. Nonetheless, none of existing methods leverage secondary reasoning to exploit results of relation prediction. To this end, we propose a novel Secondary Reasoning Framework (SRF) for DocRE. In SRF, we initially propose a DocRE model that incorporates bidirectional mention fusion and a simple yet effective evidence extraction module (incurring only an additional learnable parameter overhead) for relation prediction. Further, for the first time, we elaborately design and propose a novel secondary reasoning method to discover more relations by exploring the results of the first relation prediction. Extensive experiments show that SRF achieves SOTA performance and our secondary reasoning method is both effective and general when integrated into existing models.

## introduction relation extraction (re), which aims to identify semantic relations between head-tail entity pairs in a single sentence, is one of the most fundamental tasks in information extraction (zeng et al., 2015; 1 code is available at https://github.com/zelf0914/srf . ## methodology an illustration of our srf is shown in figure 2 , which consists of several main components: ## conclusion we propose a novel secondary reasoning framework srf for docre. in srf, we propose a bidirectional mention fusion method and design a simple yet effective evidence extraction way by introducing only a learnable parameter to jointly perform relation prediction. on this basis, for the first time, we propose a novel secondary reasoning idea that is carefully designed to further explore results of the first relation prediction. experiments demonstrate srf achieves sota performance. further analyses demonstrate that secondary reasoning is effective and general. in future we will apply our framework to more models and explore more encoders, and conduct more extensive experiments on a wider range of datasets.