Joint Document-Level Event Extraction via Token-Token Bidirectional Event Completed Graph (2023)

Abstract: We solve the challenging document-level event extraction problem by proposing a joint exaction methodology that can avoid inefficiency and error propagation issues in classic pipeline methods. Essentially, we address the three crucial limitations in existing studies. First, the autoregressive strategy of path expansion heavily relies on the orders of argument role. Second, the number of events in documents must be specified in advance. Last, unexpected errors usually exist when decoding events based on the entity-entity adjacency matrix. To address these issues, this paper designs a Token-Token Bidirectional Event Completed Graph (TT-BECG) in which the relation eType-Role1-Role2 serves as the edge type, precisely revealing which tokens play argument roles in an event of a specific event type. Exploiting the token-token adjacency matrix of the TT-BECG, we develop an edge-enhanced joint document-level event extraction model. Guided by the target token-token adjacency matrix, the predicted token-token adjacency matrix can be obtained during the model training. Then, extracted events and event records in a document are decoded based on the predicted matrix, including the graph structure and edge type decoding. Extensive experiments are conducted on two public datasets, and the results confirm the effectiveness of our method and its superiority over the state-of-the-art baselines.

## introduction document-level event extraction aims to recognize events in a document with pre-defined types and corresponding event arguments, which includes entity extraction, entity-event correlation mapping, event type recognition, and argument role judgment. sentence-level event extraction approaches (sha et al., 2018; yang et al., 2019; lu et al., 2021; * corresponding author. @xcite are difficult to deal with the problem of arguments across sentences. also, a document usually contains multiple events without clear boundaries, and the corresponding descriptions may interact together, increasing the challenges of extracting event information accurately. figure 1 demonstrates a document example where we can summarize the following challenges in event extraction. (1) arguments across sentences. the event e 1 of eu (equityunderweight) type is triggered by the "reduced" with most of the arguments in s 5 , yet the argument acting as "laterhold-ingshares" is scattered in s 8 , and the information describing other events are in s 6 and s 7 ; that is, the descriptions of different events are mixed together. (2) multiple events. we must determine the accurate number of events. also, considering that tokens reflecting the meaning of "reduce" appear more than once, it will be a problem to determine that there is only one event of the eu type. (3) more noise. not all entities with the same characteristics act as arguments. for example, both "january 8, 2009" and "january 7, 2009" in s 5 are time entities. the former does not act as the argument, while the latter does. along this line, two strategies have been adopted in existing documentlevel event extraction models. previous studies on document-level event extraction generally adopted the pipeline pattern @xcite @xcite @xcite , which decomposes the task into the following sub-tasks: (1) entity extraction (obtaining candidate arguments from a document), (2) event type recognition (judging the event types involving in the document and clarifying the ontology of each event type), and (3) multi-event and corresponding argument identification according to the recognized event types in the sub-task (2). therefore, error propagation exists in these methods. in terms of implementation strategies, the methods based on graph decoding are mainly divided into entity-based directed acyclic graph @xcite @xcite and pseudo-trigger-aware pruned complete graph @xcite . the former employed a path-expanding autoregression strategy but relied heavily on the specified argument role order of a triggered event type, resulting in the pipeline pattern only being adopted and huge training costs. to narrow down candidate entities for argument recognition, the latter established the mappings between entities and events with the idea of the clique. since triggers are not marked in the corpus, the concept of pseudo-triggers was proposed, and a pruned complete graph was constructed based on the selected pseudo-triggers. nevertheless, the constructed graph cannot be fully decoded into corresponding event records due to sharing the same pseudo triggers; that is, the gold training target of the model has errors, hence will seriously affect model learning and the final event record decoding based on the predicted graph. to realize the joint document-level event extraction, this paper devises a token-token bidirectional event completed graph (tt-becg) with the relation etype-role 1 -role 2 as the edge type, accurately revealing which tokens play argument roles in an event of a specific event type. thus, all tasks for document-level event extraction by the pipeline pattern are integrated into the graph. employing the adjacency matrix of tt-becg, we develop an edge-enhanced joint document-level event extraction model (edee). specifically, based on the adjacency matrix of target tt-becg (generated according to the corpus), the model is trained to approximate it and obtain the predicted tokentoken adjacency matrix. all event records can be decoded by the predicted adjacency matrix, including graph structure and edge-type decoding. therefore, the whole document-level event extraction is transformed into the prediction and decoding task of the token-token adjacency matrix, achieving the joint extraction. to sum up, the main contributions of this work are threefold. ## methodology in this paper, the document-level event extraction is converted to a prediction and decoding task for graph structure and edge type. it mainly includes three stages: (1) constructing the target token-token bidirectional event completed graph (tt-becg) according to the training corpus, along with the corresponding adjacency matrix; (2) designing the model for training and obtaining the predicted token-token adjacency matrix; (3) decoding the predicted adjacency matrix, generating all events and event records contained in documents. ## additional analysis and discussions to further investigate the impact of each component on event extraction performance, we conducted additional experiments, including single & multiple events and ablation.