Scented- EAE : Stage-Customized Entity Type Embedding for Event Argument Extraction (2024)

Abstract: Existing methods for incorporating entities into EAE rely on prompts or NER. They typically fail to explicitly explore the role of entity types, which results in shallow argument comprehension and often encounter three issues: (1) weak semantic associations due to missing role-entity correspondence cues; (2) compromised semantic integrity from abandoning context after recognizing entities regardless of their types; (3) one-sided semantic understanding relying solely on argument role semantics. To tackle these issues, we propose Scented-EAE, an EAE model with stage-customized entity type embedding to explicitly underscore and explore the role of entity types, thus intervening in argument selection. Specifically, at the input stage, we strengthen semantic associations by prompting role-entity correspondence after extending a non-autoregressive decoder as part of the encoder. At the intermediate stage, we preserve semantic integrity by optimizing our proposed BIO-aware NER and EAE via a novel IPE joint learning. At the output stage, we expand semantic understanding dimensions by determining arguments using span selectors from argument roles and entity types. Experiments show that our model achieves state-of-the-art performance on mainstream benchmarks. In addition, it also exhibits robustness in low-resource settings with the help of prompts and entity types.

## introduction event extraction (ee) is a vital task in natural language processing (nlp), which usually consists of two main subtasks: event detection (ed) and event argument extraction (eae) @xcite . given that significant progress has been made in ed through prior research @xcite @xcite , this paper aims to address the enduring challenges in eae. eae focuses on extracting arguments for each argument role in the event from the text. as can be observed in figure 1 , in the movement.transport event triggered by land, the agent is "bozos," the artifact is "cubans," and the destination is "shores." recent studies @xcite @xcite indicate that incorporating entity knowledge into eae can improve its performance, which is primarily achieved through prompts or named entity recognition (ner) techniques. however, it is crucial to highlight that both prompts and ner fail to explicitly underscore and explore the role of entity types, causing a lack of in-depth perception of entity type semantics. this deficiency often leads to a shallow and inadequately enriched comprehension of arguments, ultimately reducing the accuracy and generalization of argument extraction. hence, we consider explicitly introducing entity types into each stage of eae to direct the model's attention towards content that aligns with entity type semantics related to argument roles. this in-1 5222 tervention helps filter out more plausible results from the outset, thus mitigating potential errors and simplifying the selection process. to maximize the effectiveness of this introduction, we focus on the existing issues at each stage due to the insufficient incorporation of entity types. specifically, (1) at the input stage, prompts fail to include genuine entity type labels and role-entity correspondence in argument slots, often resulting in weak semantic associations between labels; (2) at the intermediate stage, ner tends to discontinue attention to complete context after determining candidate argument spans, usually leading to compromised contextual semantic integrity; (3) at the output stage, general argument selection solely relies on understanding argument role semantics while neglecting the comprehension in entity type dimension, bringing about one-sided semantic understanding of decisions. the above issues eventually hinder eae models from reaching better performance. in this paper, we propose scented-eae, an eae model with stage-customized entity type embedding. based on the characteristics of each stage in eae, our model customizes the integration of entity types to solve the aforementioned three problems, respectively. concretely, at the input stage, we aim to strengthen semantic associations through event-specific prompts with argument role labels, entity type labels, and role-entity correspondence. the intuition behind this is that prompting these labels and correspondence in argument slots can enrich argument semantics and enhance the interaction between roles and entities. for instance, when matching arguments for the "destination" role, entities of the "location" type are more suitable than those of the "person" type. additionally, to further capture the relationship between context and prompts when encoding the inputs, we extend a non-autoregressive decoder as part of the encoder to reinforce feature awareness. at the intermediate stage, we optimize a bioaware ner and eae via "implicit plus explicit (ipe)" joint learning to preserve semantic integrity. "implicit" refers to regular multi-task knowledge sharing, while "explicit" involves co-learning specific bio tag parameters for both bio-aware ner and eae. through ipe joint learning, our model enables each token to sense its bio tag semantics, thus enlarging semantic gaps and dilimiting boundaries between arguments of different entity types. at the output stage, we extend semantic understanding dimensions by using argument span selec-tors in two dimensions: argument roles and entity types. sparked by the empirical evidence that multiple model voting outperforms a single one @xcite , we make a joint decision on argument answers from both comprehension dimensions to enhance their accuracy. experiments show that scented-eae achieves state-of-the-art performance on three mainstream benchmarks. our base model reaches an improvement of +2.9%, +3%, and +2% f1 score over other base-scale models, while our large model obtains +1.8%, +2.2%, and +0.9% f1 gains over other large-scale ones. it also exhibits robustness in lowresource settings due to prompts and entity types. in summary, our contributions are as follows: • we propose scented-eae, an eae model with stage-customized entity type embedding to explicitly underscore and explore the role of entity types. this integration enriches the comprehension of arguments, thus optimizing the argument selection process and mitigating potential errors. ## methodology as illustrated in figure 2 , our model is transformerbased @xcite and consists of three core modules: context-template encoding, ipe joint learning, and span selector decoding. details of each module will be provided sequentially. ## experiment in this section, we conduct experiments to evaluate the performance of scented-eae. ## ablation and analysis in this section, we analyze the effect of different proposed techniques in scented-eae. ## conclusion in this paper, we propose scented-eae, an eae model with stage-customized entity type embedding to intervene in argument selection and mitigate potential errors. this model achieves state-ofthe-art performance on three benchmarks and also shows robustness in low-resource settings. in the future, from a strategic perspective, we anticipate the potential for extending the concept of tailoring integrated, domain-specific knowledge functionalities across various domains. at a granular level, our focus lies in integrating additional attributes of entities into eae, including but not limited to entity relationships.