TRUE - UIE : Two Universal Relations Unify Information Extraction Tasks (2024)

Abstract: Information extraction (IE) encounters challenges due to the variety of schemas and objectives that differ across tasks. Recent advancements hint at the potential for universal approaches to model such tasks, referred to as Universal Information Extraction (UIE). While handling diverse tasks in one model, their generalization is limited since they are actually learning task-specific knowledge.In this study, we introduce an innovative paradigm known as TRUE-UIE, wherein all IE tasks are aligned to learn the same goals: extracting mention spans and two universal relations named NEXT and IS. During the decoding process, the NEXT relation is utilized to group related elements, while the IS relation, in conjunction with structured language prompts, undertakes the role of type recognition. Additionally, we consider the sequential dependency of tokens during span extraction, an aspect often overlooked in prevalent models.Our empirical experiments indicate that TRUE-UIE achieves state-of-the-art performance on established benchmarks encompassing 16 datasets, spanning 7 diverse IE tasks. Further evaluations reveal that our approach effectively share knowledge between different IE tasks, showcasing significant transferability in zero-shot and few-shot scenarios.

## introduction information extraction (ie) refers to the task of automatically extracting structured knowledge, including entities, relations, events, and sentiments, from unstructured textual data. the primary aim is to condense text into structured, machine-friendly formats, aiding downstream tasks such as question answering @xcite and sentiment analysis @xcite . in the era of large language models (llms), structured knowledge enhances, validates, and * corresponding author figure 1 : true-uie's superiority over usm: unifying its framework with (1) structure language prompts and (2) only two relations, is (yellow) and next (blue), circumventing the inconsistent learning objectives encountered by usm. in this paper, we introduce true-uie, two universal relations unify information extraction tasks, a novel approach distinguishes itself from prior work by modeling all information extraction tasks as a common task, with the aim of conducting two universal relation extractions. this achievement marks a paradigm shift towards the applicability of universal model outputs, moving away from outputs tailored to specific tasks. the success of true-uie hinges on two distinct designs: (1) structure language prompt: the structured information of schemes is preserved, and placeholders for the is relation are left for target mentions in the text. for instance, in the task of relation extraction, we organize prompts @xmath0, in contrast to usm which separately enumerate entity and relation types. (2) only two relations are employed: is and next. the is relation aligns spans with corresponding placeholders in the prompt. as depicted in figure 1 , the entity "hartwig fischer" is linked to the entity type "people" in the triplet scheme people work for organization, indicating that "hartwig fischer" is involved in a relation of type work for and is categorized as "people". on the other hand, the next relation establishes a connection between the current span and the subsequent span within the same structural knowledge instance. for instance, "hartwig fischer" is linked to "hamburg" through the next relation, indicating their membership in the same triplet. using this approach, the is relation is utilized to identify span types, while the next relation groups these spans effectively. additionally, this method tackles the challenge of a span appearing in several instances of the same knowledge type, a common challenge in overlapping relation extraction. @xcite . this is also why usm must employ the green relation in the re task. we conducted comprehensive experiments on 16 datasets covering 7 ie tasks, including flat ner, relation extraction, event extraction, sentiment extraction, nested ner, discontinuous ner, and open information extraction. these experiments demonstrate that true-uie surpasses both state-of-theart task-specific and universal ie models across all datasets. additionally, further zero-shot and fewshot experiments indicate that true-uie's universal relations enable more effective knowledge transfer across tasks. ## related work information extraction (ie) is the task of extracting relevant spans or tuples of spans from plain text. there are various specific ie tasks, including flat/nested/discontinuous named entity recognition @xcite , relation extraction @xcite , event extraction @xcite , sentiment extraction @xcite , and open information extraction @xcite . for an extended period, researchers have focused on devising task-specific and independent methods to address these diverse ie tasks. however, in recent years, the emergence of pretraining techniques has sparked considerable interest in pretraining a versatile model capable of handling multiple ie tasks. @xcite were the first to propose a universal approach to tackling different ner tasks. @xcite unified various aspect-based sentiment analysis tasks. @xcite introduced uie, which employs a structured extraction language to frame all ie tasks. building upon uie, cong et al.2023 incorporated meta-pretraining to enhance the model's ability to extract complex structures. in contrast to uie's use of a sequence-to-sequence structure to directly generate diverse target information structures, borrowing the idea from token pair linking @xcite @xcite , usm @xcite introduces three unified token linking operations to capture the skills of structuring and conceptualizing. similarly, utc-ie @xcite decomposes several ie tasks into token pair classification tasks, utilizing the starting and ending tokens to locate spans, and using start-to-start and end-to-end token pairs to establish relations. uniex @xcite also uniformly dissects all extraction objectives into joint span detection, classification, and association problems through a unified extractive framework. however, existing generative @xcite @xcite or token pair linking methods @xcite @xcite still struggle to unifying all information extraction (ie) tasks into a single learning objective, thus maximizing knowledge sharing and generalization. in contrast, our proposed true-uie utilizes two universal relations to harmonize all tasks. ## methodology information extraction is the process of extracting knowledge from unstructured textual sources. the primary objective of uie is to establish a single, universal model that can handle various information extraction tasks. the challenges of current sota method usm encompass two main dimensions: (1) adapting the model to address the continually evolving complexities of information extraction, particularly in contexts where discontinuities and overlapping issues emerge; (2) enhancing the model's generalization capabilities to ensure a broader degree of knowledge transferability and sharing across diverse tasks. in this section, we begin by outlining the overall architecture and core principles of true-uie. subsequently, we elucidate how true-uie addresses the aforementioned challenges. this entails two pivotal ideas: first, the introduction of a structural language prompt. by incorporating structured information from the schema into the prompt, we aim to enhance the model's comprehension of tasks and alleviate its learning burden. second, utilizing two universal relational edges in conjunction with the structural prompt, we manage to unify seven ie tasks, transmuting them into a unified linking task with universal scheme. this strategy seeks to maximize the potential for knowledge to be shared seamlessly across tasks. lastly, we introduce the main mathematical formulas and training objectives involved in the model. ## experiment in this section, comprehensive experiments are undertaken in both the supervised setting and fewshot/zero-shot scenarios. we also provide ablation study on each component of true-uie in appendix. ## conclusion in this study, we've introduced an innovative approach called true-uie, which presents a unified framework for various information extraction (ie) tasks. by leveraging only two universal relations, namely is and next, we have established a consistent methodology across all ie tasks. this ensures that all components and definitions within the method remain uniform for different ie tasks, and can be applied to tasks such as discontinuous ner and open information extraction that are challenging for existing top-performing methods. the experimental results demonstrate that true-uie achieves state-of-the-art performance across 7 ie tasks and 16 datasets. it also showcases robust generalization capabilities in scenarios involving zeroshot and few-shot transfers. notably, true-uie offers both adaptable task scalability and the ability to seamlessly transfer pre-trained knowledge to novel tasks. we hope that true-uie can drive further development in the field of uie to better explore the relevant knowledge between tasks. event extraction demonstrates that this design effectively enhances the unification of learning objectives, allowing knowledge gained in ner to be shared across the relation extraction and event extraction tasks.