Intra-Event and Inter-Event Dependency-Aware Graph Network for Event Argument Extraction (2023)

Abstract: Event argument extraction is critical to various natural language processing tasks for providing structured information. Existing works usually extract the event arguments one by one, and mostly neglect to build dependency information among event argument roles, especially from the perspective of event structure. Such an approach hinders the model from learning the interactions between different roles. In this paper, we raise our research question: How to adequately model dependencies between different roles for better performance? To this end, we propose an intra-event and inter-event dependency-aware graph network, which uses the event structure as the fundamental unit to construct dependencies between roles. Specifically, we first utilize the dense intra-event graph to construct role dependencies within events, and then construct dependencies between events by retrieving similar events of the current event through the retrieval module. To further optimize dependency information and event representation, we propose a dependency interaction module and two auxiliary tasks to improve the extraction ability of the model in different scenarios. Experimental results on the ACE05, RAMS, and WikiEvents datasets show the great advantages of our proposed approach.

## introduction event argument extraction (eae) aims to identify the arguments of a given event and recognize the specific roles they play, which is one of the important subtasks of event extraction (ee). as illustrated by the example in figure 1 , given a disperseseparate event triggered by shipment, an event argument extractor is expected to identify russia, arms, armenia as the event arguments and predict their roles as (transporter, origin), passenger, and destination, where the russia argument plays the roles of transporter and origin respectively. russia has announced the details of a @xmath0, a relatively rare move likely connected with russia 's ongoing tension with turkey. event type: movement.transportperson.disperseseparate transporter passenger origin destination event type: movement.transportartifact.smuggleextract the qataris had paid @xmath1, and saudi arabia sent thousands of kalashnikovs and millions of rounds of ammunition it had bought, sometimes with the cia's help. by late 2013, the cia was working directly with saudi arabia , the united arab emirates and other nations to arm and train small groups of rebels and send them across the border into syria. ## methodology in this section, we first formulate the eae task, then introduce the overall framework of our model, as illustrated in figure 2 , which contains four core components: role-aware encoding, bi- ## conclusion in this paper, we propose a novel method based on role dependencies for eae. specifically, we first utilize the intra-event graph and the inter-event graph retrieved from the intra-event graph to automatically construct intra-event and inter-event dependencies, and then fully learn and model the dependencies of roles in different scenarios through two different gcns. to further optimize dependency information and event representation, we propose a dependency interaction module and two auxiliary tasks to improve the argument extraction ability of the model in different scenarios. we conduct extensive experiments on three eae datasets and compare our model with several strong baselines. the results show that our model achieves sota performance and outperforms all baselines in different scenarios. in further work, we are interested in constructing dependency information for other information extraction tasks and would like to apply our model to these tasks, such as named entity recognition and relation extraction.