GEMS : Generation-Based Event Argument Extraction via Multi-perspective Prompts and Ontology Steering (2025)

Abstract: Generative methods significantly advance event argument extraction by probabilistically generating event argument sequences in a structured format. However, existing approaches primarily rely on a single prompt to generate event arguments in a fixed, predetermined order. Such a rigid approach overlooks the complex structural and dynamic interdependencies among event arguments. In this work, we present GEMS, a multi-prompt learning framework that Generates Event arguments via Multi-perspective prompts and ontology Steering. Specifically, GEMS utilizes multiple unfilled prompts for each sentence, predicting event arguments in varying sequences to explicitly capture the interrelationships between arguments. These predictions are subsequently aggregated using a voting mechanism. Furthermore, an ontology-driven steering mechanism is proposed to ensure that the generated arguments are contextually appropriate and consistent with event-specific knowledge. Extensive experiments on two benchmark datasets demonstrate that GEMS achieves state-of-the-art performance, particularly in low-resource settings. The source code is available at: https://github.com/AONE-NLP/EAE-GEMS

## introduction event argument extraction (eae) is a crucial yet challenging task in natural language understanding (nlu), focused on identifying role-specific spans of text within a given event @xcite @xcite . for instance, consider the sentence from the ace05 dataset, pearl was murdered by terrorists in pakistan. the verb "murdered" triggers a "life.die" event, and the eae task aims to identify "pearl", "terrorists", and "pak- ## related work in recent years, significant progress has been made in the task of event argument extraction (eae). early approaches predominantly relied on multi-label classification methods, which focus on identifying argument spans and assigning them corresponding role labels. these approaches often incorporate auxiliary syntactic structures @xcite @xcite or model semantic relationships among event elements through sophisticated network architectures @xcite @xcite @xcite @xcite . while these methods have improved performance, they typically lose focus on the broader context once candidate argument spans are identified, leading to a loss of contextual semantic integrity. to address these limitations, sequential generation methods focus on leveraging complete contextual information throughout the extraction process to guide the model's decoding. these methods emphasize carefully designed prompts that direct the generation of sequences centered around arguments and their roles, transforming the argument extraction task into a sequence-to-sequence generation problem. early research employed linearized prompts @xcite , which directly concatenate arguments with their corresponding roles in a target format. to better capture the event-based organizational structure of argument roles, @xcite incorporated event structures to establish role dependencies, combining event roles with soft prompts. ad-ditionally, @xcite ; @xcite constructed event elements in a linearized target format using a structured templates. more recent advancements have focused on incorporating richer representations of argument roles and event structures by leveraging discrete prompts @xcite @xcite . these prompts involve manually designed, human-introspected templates tailored to each event type, which enhance the models ability to capture the nuanced semantic relationships among event roles @xcite @xcite . simultaneously, several studies have introduced resourceenhanced prompts, drawing on external auxiliary resources such as syntactics and abstract meaning representation (amr) structures @xcite @xcite . these resource-enhanced approaches further enrich the context and event schemas used during extraction, improving the quality of argument role representation. despite the improvements in performance, these methods reliance on rigid, predefined prompts limits their flexibility in adapting to the interdependencies of event arguments, leading to fluctuations in performance. to overcome these limitations, recently, prompt ensembling, a method of using multiple prompts during inference, has been shown to reduce performance variations and enhance the generalization of language models on downstream tasks @xcite . it leverages the complementary advantages of different prompts to boost prediction stability and consistency across various linguistic and semantic contexts @xcite . inspired by traditional ensemble learning in machine learning and deep learning @xcite , prompt ensembling varies in how it aggregates predictions from different prompts, with methods ranging from simple averaging of predicted probabilities or logits @xcite to more sophisticated mechanisms like weighted averaging @xcite @xcite and majority voting @xcite . ## conclusion in this paper, we present gems, a generationbased framework for eae that leverages multiperspective prompts and ontology-guided steering. by using multiple unanswered prompts to predict event arguments in different orders, gems effectively captures the interactions among event elements. the ontology-driven steering mechanism, implemented through a dual-branch crossattention process, ensures that the generated arguments are contextually relevant and aligned with event-specific knowledge. extensive experiments on the ace05-e and ere-en datasets show that