Document-Level Relation Extraction with Global Relations and Entity Pair Reasoning (2025)

Abstract: Document-level relation extraction (DocRE) aims to extract structured relational triples from unstructured text based on given entities. Existing methods are mainly categorized into transformer-based models and graph-based models. While transformer-based models capture global contextual information, they typically focus on individual entity pairs, making it challenging to capture complex interactions between multiple entity pairs. Graph-based models build document graphs using entities or sentences as nodes for reasoning but often lack explicit mechanisms to model fine-grained interactions between entity pairs, limiting their ability to handle complex relational reasoning tasks. Additionally, previous research has not considered predicting all possible relations in advance to assist with DocRE tasks. To address these issues, we propose a new framework namely **GREP** (**g**lobal **r**elations and **e**ntity **p**air reasoning) for DocRE tasks. GREP leverages the global interdependencies between entity pairs to capture fine-grained interactions and perform multi reasoning at the entity pair level. In addtion, GREP for the first time proposes an auxiliary task that predicts all possible relations in advance that exist in a document, which enables the model to filter out the most unlikely relations. Experimental results on widely-used datasets demonstrate that our model achieves state-of-the-art performance. Code is available at https://github.com/yanyi74/GREP.

## introduction document-level relation extraction (docre) aims to extract structured relation triples from unstructured text based on given entities. early studies primarily focused on sentence-level relation extraction @xcite @xcite , which predicts relations between entities within a single sentence. however, docre is more 1 our code: https://github.com/yanyi74/grep . currently, two main approaches are employed to tackle docre: transformer-based models and graph-based models @xcite . transformer-based models leverage self-attention mechanisms to capture global contextual information and are adept at handling long-distance dependencies @xcite . graphbased models construct document graphs by treating entities, mentions, or sentences as nodes @xcite and employ graph convolutional network (gcn) @xcite for reasoning on the graphs. however, while these methods implicitly model interactions between entities, they often ignore direct interactions between entity pairs and lack an explicit mechanism to model such interactions, which introduces limitations in complex relational reasoning tasks. as a multi-label classification task, identifying the relations between entities in docre usually requires one-hop or multi-hop reasoning on entity pairs, as shown in figure 1 . for instance, inferring the relation between philip and the elizabeth ii is complex since the document does not explicitly ex-press their relationship. the model needs to reason over the multi-hop relation paths between (united kingdom, member of, commonwealth) and (commonwealth, chairperson, elizabeth ii) to identify the relation (united kingdom, head of state, elizabeth ii). further, by combining the other triples in figure 1 , the model can finally deduce that the relation between philip and elizabeth ii is "spouse". by constructing a graph based on entity pairs in figure 1 , the reasoning paths between entities can be explicitly captured, thereby enhancing the model's ability to perform multi-step reasoning. in addition, in a document, an entity pair may involve multiple different relations, which are often interrelated rather than independent. therefore, we argue that if all relations can be predicted in advance for this document (e.g., the global relations as shown in figure 1 ), it could provide a more comprehensive reasoning context for the model. this enables the model to more fully cover all possible relations when predicting the relations of an entity pair, which is crucial for docre multi-label classification tasks. nevertheless, previous work has never considered predicting all relations in advance to assist with document-level extraction tasks. based on these observations, we propose a new framework namely grep (global relations and entity pair reasoning) for document-level relation extraction. first, to address the first issue of better modeling the interactions between entity pairs, we utilize the global interdependencies between entity pairs to perform multi-step reasoning at the entity pair level, where entity pairs in a document are constructed into a graph that contains all reasoning paths between entity pairs. this approach captures deeper semantic associations and fine-grained interaction information. second, to address the second issue, we for the first time propose a new auxiliary task: predicting all possible relations that exist in a document. by analyzing the relation types that may occur, this task enables the model to focus on the relations present in a document. through the above two strategies, our model not only accurately identifies relations for entity pairs with multi-hop reasoning paths but also effectively reduces interference from non-existent relations, optimizing the final relation prediction performance. moreover, building on previous works @xcite @xcite , which commonly incorporate evidence retrieval in docre, we also introduce evidence retrieval as an auxiliary task to focus on key sentences in documents. in summary, the main contributions of this paper are as follows: ## problem definition and each mention @xmath0. the goal is to predict the relations between all entity pairs (e s , e o ) from a predefined set of relations r ∪ {n a}, where n a signifies the absence of relation for an entity pair. for each entity pair (e s , e o ) with a non-na relation, we define its evidence v s,@xmath1. evidence annotations may be provided during training, depending on the dataset, but are not available during inference. ## methodology our grep model consists of three main modules as shown in figure 2 : entity pair reasoning module, which leverages global dependencies between entity pairs to construct an entity pair graph for effective reasoning; evidence extraction module, which guides the model's focus to key sentences in the document; and global relation prediction module, which predicts all potential relations in the document to filter out irrelevant ones and enhance overall relation extraction performance. ## conclusions in this paper, we propose a novel docre framework based on global relations and entity pair reasoning. we first introduce a new task specifically for docre that predicts all possible relations that exist in a document, helping to filter out the most unlikely relations. the task is simple but effectively enhances relation extraction performance and can be incorporated into other docre models. further, we propose to construct an entity pair graph for a document to capture fine-grained interactions and perform multi-step reasoning at the entity pair level. empirical studies demonstrate the effectiveness of our method, outperforming previous sota models.