Label Drop for Multi-Aspect Relation Modeling in Universal Information Extraction (2025)

Abstract: Universal Information Extraction (UIE) has garnered significant attention due to its ability to address model explosion problems effectively. Extractive UIE can achieve strong performance using a relatively small model, making it widely adopted. Extractive UIEs generally rely on task instructions for different tasks, including single-target instructions and multiple-target instructions. Single-target instruction UIE enables the extraction of only one type of relation at a time, limiting its ability to model correlations between relations and thus restricting its capability to extract complex relations. While multiple-target instruction UIE allows for the extraction of multiple relations simultaneously, the inclusion of irrelevant relations introduces decision complexity and impacts extraction accuracy. Therefore, for multi-relation extraction, we propose LDNet, which incorporates multi-aspect relation modeling and a label drop mechanism. By assigning different relations to different levels for understanding and decision-making, we reduce decision confusion. Additionally, the label drop mechanism effectively mitigates the impact of irrelevant relations. Experiments show that LDNet outperforms or achieves competitive performance with state-of-the-art systems on 9 tasks, 33 datasets, in both single-modal and multi-modal, few-shot and zero-shot settings.

## introduction information extraction (ie) @xcite tasks, both single-modal and multi-modal, encompass a wide variety of domains and relations between entities, leading to a highly diversified landscape. however, this diversification poses a significant challenge known as model explosion, which refers to the proliferation of models required to handle the diverse structures and relations present in different ie tasks. traditionally, task-specific models @xcite @xcite @xcite @xcite have been developed to address the unique requirements of individual tasks. however, this approach is not scalable and becomes increasingly impractical as the number of tasks and their complexity grow. to tackle the issue of model explosion, universal information extraction (uie) @xcite has emerged as a promising paradigm. uie aims to develop models that can extract information across different domains and relations, in both single-modal @xcite and multimodal @xcite setting, without relying on task-specific models for each individual task. by leveraging shared knowledge, uie models can generalize well to various ie tasks, reducing the need for a multitude of specialized models. generative uie @xcite approaches have been explored, but their reliance on large generative models as the foundation limits their efficiency. these models suffer from computational complexity and resource requirements, hindering their practical applicability. in contrast, extractive uie @xcite approaches have gained popularity due to their ability to achieve strong performance using relatively small models. single-target instruction uie @xcite allows for the extraction of one type of relation at a time. while it excels in accuracy for simple relations, its limited efficiency and inability to model correlations between relations restrict its applicability to more complex ie tasks. to address these limitations, multiple-target instruction @xcite has been proposed, enabling the extraction of multiple relations simultaneously. this approach aims to model correlations between relations and improve extraction efficiency. however, the incorporation of irrelevant relations introduces decision complexity and can have ramifications on extraction accuracy. hence, to address the challenges in multirelation extraction, we propose ldnet, a novel approach that leverages multi-aspect relation modeling and a label drop mechanism. in ldnet, we assign different relations to different levels for understanding and decision-making. this approach allows the model to capture the unique characteristics and nuances of each relation separately. by organizing relations into distinct levels, ldnet reduces decision confusion, enabling more accurate and reliable extraction results. additionally, ld-net incorporates a label drop mechanism to address the impact of irrelevant relations. during the extraction process, ldnet selectively drops irrelevant labels, focusing on the most relevant relations for extraction. this mechanism helps mitigate the interference caused by irrelevant relations, ensuring that the model can concentrate its attention and resources on extracting the necessary and meaningful information. by filtering out noise and irrelevant signals, ldnet enhances the overall extraction performance and reduces the potential for false positives. to assess the effectiveness of ldnet, we conduct extensive experiments on a diverse range of ie tasks and benchmark datasets, both single-modal and multi-modal. the evaluation covers few-shot and zero-shot settings to examine the generalization capability of ldnet. the results demonstrate that ldnet outperforms or achieves competitive performance compared to previous state-of-the-art systems across 9 tasks and 33 datasets. our contribution 1) we propose ldnet, a novel approach that leverages multi-aspect relation modeling and a label drop mechanism. 2) we employ model transfer learning, a valuable strategy for further enhancing model performance across various datasets. 3) we conduct experiments on 33 datasets across 9 tasks, in both single-modal and multi-modal, few-shot and zero-shot settings, and the results demonstrate the superiority of ldnet. extractive uie dygie++ @xcite utilizes a dynamic span graph to model longrange relations, and with graph propagation, the model can disambiguate challenging entity mentions. uniex @xcite converts ie tasks into a token-pair problem, develops a traffine attention mechanism to integrate heterogeneous factors, and obtains the extraction target via a scoring matrix. these single-target extractive uie approaches can achieve strong performance using a relatively small model; however, they lack the ability to model correlations between relations, thus limiting their capability to extract complex relations. oneie @xcite ) also uses a span graph, but unlike dygie++, it incorporates global features and adopts a crf-based tagger to remove the constraint on the length of extracted mentions. umgf @xcite adopts a unified intramodal and inter-modal graph fusion method to represent visual and textual features within the same embedding space. hvpnet @xcite designs pyramidal features for images, employing it is worth noting that the existing uie models have basically only conducted experiments on single-modal or multi-modal ie tasks, and have not handled single-modal and multi-modal ie tasks simultaneously like ldnet. ## methodology ldnet's overall framework is built upon a pretrained language model and an image backbone, consisting of a multi-aspect relation modeling component and a label drop mechanism, as shown in figure 1 . we formulate ie tasks as a multi-aspect spanbased relation extraction problem. specifically, we consider three kinds of relations among ie tasks: ## conclusion and discussion in this paper, we propose ldnet, a novel network that combines multi-aspect relation modeling and a label drop mechanism. ldnet assigns different relations to different levels for understanding and decision-making, thereby reducing decision confusion. by introducing the label drop mechanism, ldnet alleviates the influence of irrevelant relations. experimental results show that ldnet achieves highly competitive performance across 9 tasks, in both single-modal and multi-modal, fewshot and zero-shot settings, which verifies its effetiveness and universality.