Revisiting Event Argument Extraction: Can EAE Models Learn Better When Being Aware of Event Co-occurrences? (2023)

Abstract: Event co-occurrences have been proved effective for event extraction (EE) in previous studies, but have not been considered for event argument extraction (EAE) recently. In this paper, we try to fill this gap between EE research and EAE research, by highlighting the question that “Can EAE models learn better when being aware of event co-occurrences?” . To answer this question, we reformulate EAE as a problem of table generation and extend a SOTA prompt-based EAE model into a non-autoregressive generation framework, called TabEAE, which is able to extract the arguments of multiple events in parallel. Under this framework, we experiment with 3 different training-inference schemes on 4 datasets (ACE05, RAMS, WikiEvents and MLEE) and discover that via training the model to extract all events in parallel, it can better distinguish the semantic boundary of each event and its ability to extract single event gets substantially improved. Experimental results show that our method achieves new state-of-the-art performance on the 4 datasets. Our code is avilable at https://github.com/Stardust-hyx/TabEAE .

## introduction event argument extraction (eae) is an essential subtask of event extraction @xcite . given an input text and trigger(s) of target event(s), the eae task aims to extract all argument(s) of each target event. recently, substantial progress has been reported on eae, thanks to the success of pre-trained language models (plms). previous studies on ee commonly take event cooccurrences into account. however, recent works on eae @xcite @xcite @xcite @xcite ., † corresponding author. 2021; @xcite only consider one event at a time and ignore event cooccurrences (as illustrated in figure 1 ). in fact, event co-occurrences always exisit in text and they are useful in revealing event correlation and contrasting the semantic structures of different events. for the instance in figure 1 , there exist two events in the same context. the two events are triggered by "leaving", "become" respectively, and share the same subject "davies". it is clear that there exists a strong causal correlation between the two events. however, mainstream works on eae split the instance into two samples, which conceals this correlation. in this paper, we try to resolve this divergence between ee research and eae research, by highlighting the question that "can eae models learn better when being aware of event co-occurrences?". to address this question, we reformulate eae as a problem of table generation and extend the sota prompt-based eae model, paie @xcite , into a non-autoregressive generation framework to extract the arguments of multiple events concurrently. our framework, called tabeae, inherits the encoding, prompt construction and span selection modules from paie, but employs a novel non-autoregressive decoder for table generation. under this framework, we explore three kinds of training-inference schemes: (1) single-single, training model to extract single event at a time and infer in the same way; (2) multi-multi, training model to extract all events in parallel and infer in the same way; (3) multi-single, training model to extract all events in parallel and let the model extract single event at a time during inference. according to our experiments, the multi-single scheme works the best on 3 benchmarks (ace, rams and wikievents) and the multi-multi scheme works the best on the mlee benchmark, where the phenomenon of nested events extensively exists. besides, in-depth analysis reveals that via training tabeae to extract all events in parallel, it can better capture the semantic boundary of each event and its ability to extract single event at a time gets substantially improved. to sum up, our contributions include: ## methodology in this section, we will first give an formal definition of eae and then introduce tabeae, our solution to the task in detail. ## conclusion in this paper, we point out that recent studies on eae ignore event co-occurrences, resulting in a divergence from main-stream ee research. to remedy this, we highlight the question that "can eae models learn better when being aware of event co-occurrences" and explore it with a novel text-totable framework, tabeae, that can extract multiple event in parallel. by experimenting with 3 traininginference schemes on 4 datasets, we find that when trained to extract all event concurrently, tabeae can better capture the event semantic boundary and its ability to extract single event gets greatly improved. our work demonstrates the significance of event co-occurrence for eae and establishes a new foundation for future eae research.