Towards Better Graph-based Cross-document Relation Extraction via Non-bridge Entity Enhancement and Prediction Debiasing (2024)

Abstract: Cross-document Relation Extraction aims to predict the relation between target entities located in different documents. In this regard, the dominant models commonly retain useful information for relation prediction via bridge entities, which allows the model to elaborately capture the intrinsic interdependence between target entities. However, these studies ignore the non-bridge entities, each of which co-occurs with only one target entity and offers the semantic association between target entities for relation prediction. Besides, the commonly-used dataset–CodRED contains substantial NA instances, leading to the prediction bias during inference. To address these issues, in this paper, we propose a novel graph-based cross-document RE model with non-bridge entity enhancement and prediction debiasing. Specifically, we use a unified entity graph to integrate numerous non-bridge entities with target entities and bridge entities, modeling various associations between them, and then use a graph recurrent network to encode this graph. Finally, we introduce a novel debiasing strategy to calibrate the original prediction distribution. Experimental results on the closed and open settings show that our model significantly outperforms all baselines, including the GPT-3.5-turbo and InstructUIE, achieving state-of-the-art performance. Particularly, our model obtains 66.23% and 55.87% AUC points in the official leaderboard under the two settings, respectively,ranking the first place in all submissions since December 2023. Our code is available at https://github.com/DeepLearnXMU/CoRE-NEPD.

## introduction relation extraction (re) is a fundamental natural language processing (nlp) task, which aims to predict the relationship between two entities in a given context. usually, conventional re studies limit the context within a single sentence or document @xcite @xcite @xcite . however, since a large number of relational facts are not described in the same document @xcite , many researchers have begun to concentrate on cross-document re (core), where the given target entities (head entity and tail entity) are located in different documents @xcite @xcite . in this regard, @xcite first explore this task. they not only release the dataset co-dred, where relevant documents of target entities connected by bridge entities are organized as text paths, but also propose bert-based models for this task. however, their models suffer from the negative effect of irrelevant context in the model input and do not fully leverage the connections across text paths. to solve these issues, @xcite propose an entity-based cross-path relation inference method (ecrim). they employ an entitycentered noise filter to refine the model input and incorporate a cross-path entity relation attention to capture the connections across different text paths. unlike the ecrim, @xcite present a localto-global causal reasoning model (lgcr), which is a graph-based model using a local causality estimation algorithm to filter the noisy information. despite their success, their models still suffer from two issues. first, previous methods only consider the target entities and bridge entities, while ignoring the non-bridge entities that solely co-occur with only one target entity. according to the statistics of the codred dataset @xcite , we observe that on average each text path contains 18.8 non-target entities: 2.6 bridge entities while 16.2 non-bridge entities that may also help the relation prediction. for example, in the text path 1 of figure 1 , the target entities "europa plus" and "soviet union" text path 1 [head document] europa plus has become a famous russian radio station … [tail document] … the russian soviet republic was the most populous republic of the soviet union … text path 2 [head document] europa plus broadcasts in russian … began broadcasting in the ussr … [tail document] … the soviet union launched the venus to the moon … the official language of the union is russian … document bag entity graph target entity non-bridge entity bridge entity co-occurrence edge semantic-related edge non-target entities europa plus soviet union europa plus soviet union russian venus russian soviet republic russian radio station ussr only occur with the non-bridge entities "russian radio station" and "russian soviet republic", respectively. thus, it is difficult to correctly predict their relation due to the absence of bridge entities. if non-target entities "russian radio station" and "russian soviet republic" are semantically related, then there may also be some relationship between the target entities "europa plus" and "soviet union". therefore, intuitively, such semantic correlation can be exploited to benefit the relation prediction between target entities. second, 85% of document bags are labeled as na relation in the co-dred training set. as a result, the training dataset can only offer limited supervision signals for the model to learn non-na relations, which leads to prediction bias. to address these issues, in this paper, we propose a novel graph-based core model with non-bridge entity enhancement and prediction debiasing. as illustrated in the right part of figure 1 , we represent each input document bag with a unified entity graph, where entity nodes are initialized with bert representations. in this graph, each node indicates a target entity, bridge entity, or non-bridge entity, and two types of edges are introduced: 1) cooccurrence edges, each of which connects a target entity and a non-target entity that co-occurs with it. 2) semantic-related edges, which are used to connect any two semantic-related non-target entities. then, we utilize graph recurrent network (grn) @xcite to encode this graph, where the interdependency among connected nodes is captured based on the recurrent gating mechanism. afterward, we aggregate the entity representations learned from grn and utilize a cross-path entity relation attention module to capture the connections across text paths. besides, inspired by the studies on other nlp tasks @xcite @xcite , we propose a simple yet effective prediction debiasing strategy to calibrate the relation prediction. specifically, we introduce two prediction distributions: 1) y rela . to obtain this distribution, we fix the parameters of the original model and retrain a new classifier only using non-na instances. notably, the new classifier can avoid the negative impact of excessive na instances and achieve better prediction performance on non-na instances. 2) y bias . we mask the most important non-target entities of our entity graph as input to derive this distribution. compared with the original prediction distribution, this distribution is more biased and thus can be used for debiasing in a manner of subtraction. finally, we integrate the two newlyintroduced prediction distributions to calibrate the original ones. to the best of our knowledge, our work is the first attempt to study the prediction debiasing in this task. to investigate the effectiveness of our model, we conduct extensive experiments on both the closed and open settings of codred. experimental results and in-depth analysis show that our model significantly outperforms all competitive baselines, including the llms. particularly, compared with all submitted results since december 2023, our model ranks the first place in the official leaderboard. 2 our model in this section, we introduce our proposed model in detail. as shown in figure 2 , our model contains three important components: a bert encoder, a grn encoder, and a classifier with a cross-path entity relation attention module, and then we detail the prediction debiasing strategy used in inference. ## related work most studies on re mainly focus on the sentencelevel re @xcite @xcite @xcite and document-level re @xcite @xcite , which are committed to identifying the relation between target entities from a sentence and document, respectively. unlike these studies, in this work, we concentrate on cross-document re that aims at predicting the relation between target entities located in different documents. @xcite comprehensively investigated this task and released the first humanannotated core dataset, codred. besides, they explore an end-to-end model that jointly considers documents in text paths to predict the relation. however, it not only suffers from the negative effect of irrelevant context in text paths but also does not fully leverage the interconnections across text paths. to address the above-mentioned issues, @xcite propose entity-based cross-path relation inferencemethod (ecrim). typically, it uses an attention mechanism to capture the connection among different text paths through bridge entities, which is important to predict the final relation. recently, @xcite put forward localto-global causal reasoning (lgcr). to aggregate information over multiple text paths, they construct a global heterogeneous graph, where a local causality estimation algorithm is proposed to assess the importance of different nodes in the graph. however, their methods suffer from two limitations: 1) ignore the non-bridge entities, which exist broadly in each text path and can offer semantic associations between target entities, especially in the absence of bridge entities. 2) ignore the bias caused by the prominent number difference between na and non-na instances. in this work, we propose a graph-based model to fully exploit non-bridge entities for cross-document re. besides, along the research line of debiasing in nlp @xcite @xcite , we propose a simple yet effective prediction debiasing strategy to refine the prediction of our model. ## conclusion and future work in this paper, we propose a novel graph-based crossdocument re model. concretely, we first represent the input bag as a unified entity graph, where abun-dant non-bridge entities are introduced to provide useful information. then, we use grn to encode this graph, where the learned entity representations form the basis for subsequent relationship prediction. besides, we propose a simple yet effective debiasing strategy to refine the original prediction distribution. to the best of our knowledge, the graph-based model with non-bridge entities and our debiasing strategy has not been explored before. extensive experiments on the commonly-used dataset codred demonstrate the superiority of our model. in the future, we will study how to introduce more external knowledge to refine our model.