Open-Vocabulary Argument Role Prediction For Event Extraction (2022)

Abstract: The argument role in event extraction refers to the relation between an event and an argument participating in it. Despite the great progress in event extraction, existing studies still depend on roles pre-defined by domain experts. These studies expose obvious weakness when extending to emerging event types or new domains without available roles. Therefore, more attention and effort needs to be devoted to automatically customizing argument roles. In this paper, we define this essential but under-explored task: open-vocabulary argument role prediction . The goal of this task is to infer a set of argument roles for a given event type. We propose a novel unsupervised framework, RolePred for this task. Specifically, we formulate the role prediction problem as an in-filling task and construct prompts for a pre-trained language model to generate candidate roles. By extracting and analyzing the candidate arguments, the event-specific roles are further merged and selected. To standardize the research of this task, we collect a new human-annotated event extraction dataset including 143 customized argument roles with rich semantics. On this dataset, RolePred outperforms the existing methods by a large margin.

## introduction great progress has been made on event extraction in recent years, however, most of the existing studies still rely on hand-crafted ontologies @xcite @xcite @xcite @xcite . event ontologies such as propbank @xcite and framenet @xcite take years, even decades, to construct. at the center of such ontologies lie argument roles, which capture the ## related work event ontology construction event ontologies are a crucial prerequisite to event discovery and extraction. great efforts have been paid in previous studies to build several high-quality ontologies, such as framenet @xcite , propbank @xcite , and verbnet @xcite . however, it is costly and time-consuming to build hand-crafted ontologies. some researchers start to explore automatic ontology construction. specifically, much progress has been made in event schema induction to characterize the relationship among different events @xcite @xcite @xcite . also, several recent studies attempt to discover new event types from raw texts @xcite . nevertheless, as the center of event ontologies, argument role prediction has always been an underexplored task. related studies @xcite restrict role names to a limited vocabulary so that they fail to reflect the unique characteristics of different event types. therefore, in this paper, we study an essential but challenging task: open-vocabulary argument role prediction. event extraction @xcite has been studied since an early stage @xcite @xcite , with a few models gone beyond individual sentences to make decisions @xcite @xcite ; and (2) documentlevel event extraction has gained a lot of research attention recently @xcite @xcite @xcite . this study further explores extracting arguments scattered throughout documents. ## experiment in this section, we first study the performance on the argument role prediction task, then examine the performance on the downstream task, argument extraction. finally, we report our case analysis. ## conclusion this paper studies a challenging but essential task: open-vocabulary argument role prediction, and propose a novel unsupervised framework rolepred as a strong baseline and a carefully designed event extraction dataset for future work.