Beyond Single-Event Extraction: Towards Efficient Document-Level Multi-Event Argument Extraction (2024)

Abstract: Recent mainstream event argument extraction methods process each event in isolation, resulting in inefficient inference and ignoring the correlations among multiple events. To address these limitations, here we propose a multiple-event argument extraction model DEEIA (Dependency-guided Encoding and Event-specific Information Aggregation), capable of extracting arguments from all events within a document simultaneously. The proposed DEEIA model employs a multi-event prompt mechanism, comprising DE and EIA modules. The DE module is designed to improve the correlation between prompts and their corresponding event contexts, whereas the EIA module provides event-specific information to improve contextual understanding. Extensive experiments show that our method achieves new state-of-the-art performance on four public datasets (RAMS, WikiEvents, MLEE, and ACE05), while significantly saving the inference time compared to the baselines. Further analyses demonstrate the effectiveness of the proposed modules.

## introduction document-level event argument extraction (eae) is a key process within information extraction @xcite @xcite , focused on identifying event-related arguments and their respective roles in document-level texts. recently, the leading-edge methods for this task delve into prompt-based techniques @xcite , due to their great generalizability and competitive performance. figure 1 (a) presents an example demonstrating eae utilizing a prompt-based approach. regarding the event e 0 triggered by "bombarding", the approach defines a corresponding event prompt and identifies arguments: "government" as the killer, "a number of areas" as the victim, and "shelling" as the instrument. mainstream eae works @xcite @xcite ) can only process one event at a time. when encountering documents containing multiple events, the limitations of these single-event argument extraction (single-eae) methods become evident. (1) as shown in figure 1 (b) , for a document containing multiple events, single-eae methods have to perform numerous iterations to extract event arguments for all events, which process the same document text repeatedly, leading to inefficient extraction. (2) single-eae methods fail to capture the beneficial event correlations among multiple events @xcite . @xcite illustrates the argument overlapping phenomenon which reflects the semantic correlations among events. the event life.death and event life.surrender share the argument "government" and there exists a strong event correlation between these two events. however, single-eae methods cannot utilize such correlations. to tackle these limitations, this paper proposes a deeia (dependency-guided encoding and event-specific information aggregation) model, a multiple-event argument extraction (multi-eae) method capable of simultaneously extracting arguments for all events within the document. we construct our deeia model based on the state-ofthe-art (sota) prompt-based single-eae model paie @xcite and introduce a multi-event prompt mechanism to enable extracting arguments from multiple events simultaneously. however, our multi-eae model faces the challenge of handling more complex information as it needs to simultaneously process different triggers, arguments roles, and prompts from multiple events. this requires the model with enhanced information extraction capabilities. therefore, we design a dependency-guided encoding (de) module to guide the model in correlating the various prompts with their respective event contexts. furthermore, we propose an event-specific information aggregation (eia) module to provide event-specific contextual information for a better context understanding. figure 2 demonstrates that with the increase in the number of events within a document, the efficiency advantage of our deeia model becomes increasingly apparent. the performance surpassing single-eae baselines also demonstrates that our model effectively captures event correlations. the contributions of this paper are summarized as follows: ## related work recently, there has been an increasing interest in the task of document-level event argument extraction @xcite @xcite , a crucial component within the domain of event extraction @xcite . current methods for documentlevel eae can be classified into four main categories: (1) span-based methods, which identify candidate spans and subsequently predict their roles @xcite @xcite @xcite . however, these eae methods are single-eae methods, which can only process one event at a time. recently, prompt-based eae method, tabeae @xcite aims to capture event role output participant dzhokha... participant silva topic n/a place n/a victim sean... killer dzhokha... context: prosecutors said ... dzhokhar @xmath0... used @xmath1. p1: <@xmath2.@xmath3. p2: ... victim died at place, killed by killer. p3: ... attacker attacked target using instrument at place , decoder self-attention ffn ×n attention multiplication weighted sum [ , , . . . , , , . . . ] span selector concatenated context and prompts (99,100) (120,120) (-1,-1) (-1,-1) (148,149) (99,100) ... event-specific information aggregation ... intra-event inter-event no dependency data flow event dependencies context-enhanced vector dependency-guided ecoding co-occurrence @xcite ) and trains the model on multi-event scheme. however, tabeae requires separately processing prompts for different events, which is highly time-consuming. in this paper, we propose to extract event arguments concurrently, which significantly improves the efficiency of the eae task in multi-event documents. ## methodology in this section, we first provide a formal definition of multi-eae task. given an instance x, {e i } k @xmath4, {t i } k @xmath5, {r (e i ) } k @xmath6, where @xmath7, w 2 , . . . , w n -1 ) represents the document text with n words, k is the number of target events, and e i is the type of the i-th event. t i ⊆ x is the trigger word of the i-th event, and r (e i ) indicates the set of roles associated with event e i . the task aims to extract a set of span s i for each event e i , which satisfies ∀a (r) ∈ s i , (a (r) ⊆ x) ∧ (@xmath8. most previous eae methods are designed as single-eae methods, applicable when @xmath9. we then provide a detailed description of our deeia model, as shown in figure 3 . we first propose a multi-event prompt mechanism to enable extracting arguments from multiple events simultaneously ( § 3.1). then we propose a dependencyguided encoding module ( § 3.2) and an eventspecific information aggregation module ( § 3.3) to tackle the complexity and challenge of simultaneously extracting arguments of multiple events. ## conclusion in this paper, we propose a multi-eae model deeia, which overcomes the inefficiency limitations of traditional eae methods. the proposed dependency-guide encoding (de) module and event-specific information extraction (eia) module effectively enhances the model's ability to understand complex multi-event contexts. our extensive experiments on three public benchmarks illustrate the superiority of our model in performance and efficiency. context x: prosecutors said these items were used to help remotely -detonate the bombs february , 2013 dzhokhar tsarnaev visits silva and borrows the ruger pistol -the gun that was later used to kill mit police officer sean collier and during the shootout with police in watertown . cshellingattacker cshellingattacker event #2: life.die.unspecified trigger: kill without dependency-guided encoding victim: pred: __ no answer __ gt: sean collier ❌ killer: pred: dzhokhar tsarnaev gt: __ no answer __ with dependency-guided encoding victim: pred: sean collier gt: sean collier killer: pred: __ no answer __ gt: __ no answer __ ✔ ✔ ✔ ❌ cshellingattacker event #4: transaction.exchangebuysell trigger: borrows without event-specific information aggregation giver: pred: __ no answer __ gt: silva ❌ recipient: pred: dzhokhar gt: dzhokhar tsarnaev ❌ with event-specific information aggregation giver: pred: silva gt: silva recipient: pred: dzhokhar tsarna gt: dzhokhar tsarnaev ✔ ✔ ✔ event #3: contact.contact.meet trigger: visits without event-specific information aggregation participant1: pred: dzhokhar gt: dzhokhar tsarnaev ❌ participant2: pred: __ no answer __ gt: silva ❌ with event-specific information aggregation participant1: pred: dzhokhar tsarnaev gt: dzhokhar tsarnaev participant2: pred: silva gt: silva cshellingattacker ✔ event #1: conflict.attack.unspecified trigger: shootout without dependency-guided encoding target: pred: police gt: police instrument: pred: __ no answer __ gt: gun ❌ with dependency-guided encoding target: pred: police gt: police instrument: pred: gun gt: gun ✔ ✔ ✔ figure 6: a multi-event test case from wikievents.