Separation and Fusion: A Novel Multiple Token Linking Model for Event Argument Extraction (2024)

Abstract: In event argument extraction (EAE), a promising approach involves jointly encoding text and argument roles, and performing multiple token linking operations. This approach further falls into two categories. One extracts arguments within a single event, while the other attempts to extract arguments from multiple events simultaneously. However, the former lacks to leverage cross-event information and the latter requires tougher predictions with longer encoded role sequences and extra linking operations. In this paper, we design a novel separation-and-fusion paradigm to separately acquire cross-event information and fuse it into the argument extraction of a target event. Following the paradigm, we propose a novel multiple token linking model named Sep2F, which can effectively build event correlations via roles and preserve the simple linking predictions of single-event extraction. In particular, we employ one linking module to extract arguments for the target event and another to aggregate the role information of multiple events. More importantly, we propose a novel two-fold fusion module to ensure that the aggregated cross-event information serves EAE well. We evaluate our proposed model on sentence-level and document-level datasets, including ACE05, RAMS, WikiEvents and MLEE. The extensive experimental results indicate that our model outperforms the state-of-the-art EAE models on all the datasets.

## introduction as a crucial step of event extraction (ee), event argument extraction (eae) aims to recognize all arguments and their roles for each event in text. the recognized arguments can act as structured semantic information and greatly influence various downstream tasks @xcite @xcite . despite the * corresponding author. text : the man detonated explosives killing james mcdade. the man detonated explosives killing james mcdade attacker instrument place target. ## proposed model we represent an instance as (x, t, c), where x is the input text, t denotes the target event and c denotes the other events surrounding t within the text x. specifically, t is further represented as (e, t, r e ), where e is the event type, t is the trigger word and r e is the set of role types specific to e. similarly, c is represented as {(ẽ i , ti , r ẽi )| @xmath0. eae aims to extract the argument set a for the target event t , where each argument a (r) in a is a snippet of the text x with the role type @xmath1. as illustrated in figure 2 , we introduce our proposed model sep2f, which consists of three modules: linking construction for multiple events, linking construction for target event and two-fold fusion. sep2f follows our designed separation-and-fusion paradigm. specifically, the first two modules separate the acquisition of crossevent information and the argument extraction process of the target event. in contrast, the last module introduces our proposed fusion for the acquired cross-event information. next, we describe these modules in details. ## conclusion in this paper, we propose sep2f, a novel multiple token linking model for eae. specifically, we employ two linking modules to separate the acquisition of cross-event information and the argument extraction of a target event. in addition, we propose a novel two-fold fusion module to guarantee that the acquired cross-event information enhances the argument extraction effectively. therefore, the proposed model can leverage cross-event clues and retain the merits of single-event extraction. extensive experiments on four widely used benchmarks show our model achieves new state-of-the-art performance.