EA ^2 E : Improving Consistency with Event Awareness for Document-Level Argument Extraction (2022)

Abstract: Events are inter-related in documents. Motivated by the one-sense-per-discourse theory, we hypothesize that a participant tends to play consistent roles across multiple events in the same document. However recent work on document-level event argument extraction models each individual event in isolation and therefore causes inconsistency among extracted arguments across events, which will further cause discrepancy for downstream applications such as event knowledge base population, question answering, and hypothesis generation. In this work, we formulate event argument consistency as the constraints from event-event relations under the document-level setting. To improve consistency we introduce the Event-Aware Argument Extraction (EA ^2 E) model with augmented context for training and inference. Experiment results on WIKIEVENTS and ACE2005 datasets demonstrate the effectiveness of EA ^2 E compared to baseline methods.

## introduction document-level event argument extraction aims at identifying arguments and their roles for multiple events in a document. it is a practically more useful but more challenging task than sentence-level argument extraction @xcite @xcite because in a typical long input document events usually scatter across multiple sentences and are inherently connected. multiple events in one document are usually interconnected, and thus the arguments are under certain consistency constraints. in figure 1 , the roles of the shared argument ahmad khan rahami in multiple events are constrained because the attacker in the detonateexplode event is likely to be the identifiedrole in identifycategorize event, the event 2: identifycategorize identifiedrole rahami identifiedobject man identifier officers [s1]: the saturday night's [bombing] in new york city wounded 29 people. [s2]: prosecutors have [charged] 28year-old ahmad khan rahami with five counts of attempted murder stemming from the shootout that wounded two officers. ... [s4]: officers [recognized] the man as rahami, who opened fire, wounding two policemen before he was injured. ... [s6] police officers walk near the site where ahmad khan rahami, sought in connection with a bombing in new york, was [taken into custody] in linden, new jersey, u.s., sept. 19, 2016. event 3: arrestjaildetain detainee ahmad khan rahami jailer officers place linden event 1: detonateexplode attacker ahmad khan rahami place new york city event 4: chargeindict defendant ahmad khan rahami prosecutor prosecutors figure 1: examples of extracting arguments for multiple events in one document. the casual relation between the arrest event and the detonate event puts their arguments under consistency constraints: ahmad khan rahami, the detainee in event 3, is very likely to be the attacker in event 1. sentence-level models tend to miss the cross-sentence attacker argument in event 1. detainee in the arrestjaildetain event, as well as the defendant in chargeindict event. motivated by the one-sense-per-discourse theory @xcite ) that mentions of an ambiguous word usually tend to share the same sense in a given discourse, we hypothesize that a participant tends to play consistent roles across multiple events in the same document. however, previous work such as @xcite @xcite on document-level event argument extraction focuses on modeling each event independently and ignores the relation between events, and thus the extracted arguments of multiple events may violate the constraints from event-event relations. we call this inconsistency phenomenon. though received much attention in various areas like abstractive summarization @xcite @xcite and question answering @xcite , the inconsistency phenomenon addressed in previous research focuses on factual consistency instead of self-contained consistency as in document-level argument extraction. we approach this problem with inspiration from human behavior: while reading, humans subconsciously infer the event-event relations and correctly identify the event arguments under the perceived constraints. therefore, we refer consistent argument extraction to applying the underlying event-event relations as constraints in multi-event argument extraction. an intuitive solution to improve consistency is to incorporate explicit event-event relations into the extraction process as additional input. however, the underlying event-event relations are hard to identify and classify due to the lack of reliable resources as supervision signals, especially when the arguments are unknown. in addition, precise event-event relations may not be necessary for argument extraction when the implicit connections can already well support argument extraction. to avoid explicit modeling of event-event relations, we label the arguments of other events in the context as an implication of event-event relations. we propose an event-aware argument extraction (ea 2 e) model, which incorporates alignment-enhanced training and iterative inference. when extracting arguments, the context can be self-augmented by tagging the argument labels of other events. alignment-enhanced training implicitly introduces event awareness by pulling close the argument representation distributions under regular context and augmented context, where ground-truth argument labels of neighboring events are labeled. iterative inference explicitly encourages event awareness by augmenting the context with the extracted arguments from the last inference iteration. the advantage of this method is that no predefined event-event relation is required, nor event schema. our experiments on wikievents and ace2005 datasets show that our ea 2 e model brings improvement against previous methods. ## event-aware argument extraction motivated by the observation that introducing event-event relations benefits the consistency of event argument extraction, we propose to incorporate implicit event-event relations with an event-aware argument extraction (ea 2 e) model. as shown in figure 2 , ea 2 e contains alignmentenhanced training and iterative inference with self-augmented context. when extracting the arguments for a target event, the context is augmented by labeling the arguments from neighboring events. during training, an auxiliary training loss pulls close the event argument representations under the regular context and self-augmented context. during inference, iterative inference encourages event awareness by using the extraction arguments from the last inference iteration as inputs. ## related work sentence-level argument extraction approaches, where the event trigger and its arguments are usually located within a single sentence, cannot handle the cross-sentence trigger-argument distribution and the existence of multiple events within one document. though recent attempts on documentlevel argument extraction have gone beyond sentence boundaries, they either focus on one-eventper-document setting, or model each event independently. the most related work to ours is @xcite , which formulates the task as conditional generation following event templates and extracts arguments for each event independently, while our work focuses on the consistency among arguments for different events. the methods in @xcite and @xcite are designed for role-filler entity extraction (ree) task under the assumption that one generic template is produced for each document, while our work focuses on extracting arguments for multiple events for each document. @xcite introduces parallel prediction network that generates all possible events in parallel based on the document-aware representations, while we adopt a generative framework. @xcite model the whole document as graphs and capture the interdependency among events by tracking the extracted events with a global memory, while we introduce event awareness for interdependency without external memory modules. ## conclusion we introduce event-aware argument extraction (ea 2 e) model to improve self-contained consistency in document-level event argument extraction. we conclude that in iterative inference brings higher performance only to a certain range of iterations and alignment-enhanced training brings significant improvement with costs.