An Iteratively Parallel Generation Method with the Pre-Filling Strategy for Document-level Event Extraction (2023)

Abstract: In document-level event extraction (DEE) tasks, a document typically contains many event records with multiple event roles. Therefore, accurately extracting all event records is a big challenge since the number of event records is not given. Previous works present the entity-based directed acyclic graph (EDAG) generation methods to autoregressively generate event roles, which requires a given generation order. Meanwhile, parallel methods are proposed to generate all event roles simultaneously, but suffer from the inadequate training which manifests zero accuracies on some event roles. In this paper, we propose an Iteratively Parallel Generation method with the Pre-Filling strategy (IPGPF). Event roles in an event record are generated in parallel to avoid order selection, and the event records are iteratively generated to utilize historical results. Experiments on two public datasets show our IPGPF improves 11.7 F1 than previous parallel models and up to 5.1 F1 than auto-regressive models under the control variable settings. Moreover, our enhanced IPGPF outperforms other entity-enhanced models and achieves new state-of-the-art performance.

## introduction document-level event extraction (dee) aims to extract multiple event records from the entire document @xcite @xcite . different from sentencelevel event extraction (see) @xcite @xcite , event arguments of an event record are usually scattered across multiple sentences, while overlapping arguments contained in several event records appear more often. moreover, real-world event records always use similar but different words to report the same type of events, leading to low annotations of triggers and the requirement of trigger-free methods. the absence of triggers increases the difficulty of dee. therefore, extracting multiple event records without triggers is the main challenge of the trigger-free dee methods. most trigger-free dee methods @xcite @xcite build entity-based directed acyclic graph (edag) to autoregressively generate event arguments with their roles under a predefined order. however, it is important to note that determining the optimal order from the permutation space of possible orders has an np-hard complexity of o(n!). parallel models @xcite are presented to generate all event records and roles simultaneously to avoid the error broadcasts in a given event role order. nevertheless, parallel models are particularly prone to the inadequate training, leading to zero accuracies on certain event roles. in this paper, we propose an iteratively parallel generation method with the pre-filling strategy (ipgpf). as shown in figure 1 , event roles in an event record are generated in parallel, thereby avoiding the role order selection. meanwhile, event records are iteratively generated to leverage the historically generated records. the pre-filling strategy is further designed for the inadequate training in parallel event role generation. specially, the prefilling strategy adaptively pre-fills several event roles by the results in historically generated event records before the parallel event role generation, and then the rest roles are generated with the help of pre-filled roles. moreover, we explore the effect of event role orders on auto-regressive edag models. experiments show that a change in event role orders in these models can bring 3.7 f1 decrease over a whole dataset and 10.4 f1 decrease in multiple events scenarios. we summarize our contributions as follows: (1) we propose ipgpf, an iterative generation method that eliminates the need for selecting the order of event role generation. it achieves this through a two-stage matching that refines generated event records by leveraging historical results. (2) we design the pre-filling strategy, which successfully alleviates the inadequate training and zeroaccuracy problems in parallel event role generation. ## preliminaries we first clarify several important concepts: (1) named entity: mentions of rigid designators from text belonging to predefined semantic types such as person, location, organization etc @xcite ; (2) entity mention: a text span of entity in the document which refers to a named entity; (3) event argument: an entity playing a specific event role in event extraction; (4) event role: a predefined argument type corresponding to event arguments; (5) event record: the expression of an event that contains many event arguments with their event roles. a trigger-free doc-level event extraction task usually includes several pipeline sub-tasks. following @xcite , ipgpf finishes the event extraction by handling three sub-tasks: (1) named entity recognition (ner): extracting entity mentions as argument candidates from the document; (2) event detection (ed): judging whether there exist predefined event types in the document. due to the space limit, we put the details of ner and ed sub-tasks that are the same with @xcite to appendix a.3 and a.4. ## methodology given a document d containing n s sentences {s i } ns @xmath0, doc-level event extraction aims to generate multiple event records @xmath1, where n z is the number of ground truth event records of the document. an event record consists of n event arguments z @xmath2, a 2 i , ..., a n i ) and their event roles (r 1 i , r 2 i , ..., r n i ). as shown in figure 2 , ipgpf first extracts entity mentions as candidate arguments through named entity recognition @xcite ). the presence of predefined event types is then judged by event detection (appendix a.4). finally, event roles in an event record are generated in parallel, while event records are generated iteratively (section 3.1). to alleviate the inadequate training in parallel generation, we propose an effective pre-filling strategy (section 3.2). a symbol reminder is listed in ## related work most previous works concentrate on extracting events out of a single sentence. different neural architectures are utilized to extract events, e.g., convolution network @xcite , recurrent network @xcite , and transformer-based network @xcite . recently, some works also tried to cast the event extraction task as question answering task @xcite , or sequence-to-sequence task @xcite . however, these methods can only extract events at the sentence level, which greatly limits their application scenarios. therefore, document-level event extraction has been proposed to fully extract events from the entire document. most document-level ee methods are built upon the event triggers, with which they conduct sequence labelling @xcite or span-based prediction @xcite @xcite to identify event arguments in the document. sequence-to-sequence @xcite @xcite @xcite , and question answering @xcite paradigms are also applied. but the requirement of the annotations of event triggers are quite hard to meet in practice, due to the labeling cost and difficulty. consequently, trigger-free methods are required for document-level event extraction. for extracting multiple events without triggers, a trigger-free method is first designed to detect an event in a center sentence and extract the rest of event arguments from surrounding sentences @xcite . then a widely used entity-based directed acyclic graph (edag) generation method is proposed to better deal with multiple events extraction @xcite . several variant methods based on edag generation are presented by utilizing more meticulous feature engineering, such as heterogeneous graph feature @xcite and entity relation feature @xcite . additionally, a parallel method is proposed to avoid the error broadcast in edag generation @xcite , and an efficient model is designed to lighten the model and accelerates the decoding speed @xcite . ## conclusion towards the event records extraction in the dee task, we propose a novel iteratively parallel generation method with the pre-filling strategy (ipgpf). ipgpf generates event roles in parallel and iteratively generates event records with the help of the pre-filling strategy. experiments demonstrate that ipgpf successfully alleviates the inadequate training of parallel generation and avoids the event role order selection in auto-regressive generation methods. in the future, we will work on one-stage generation models to avoid the error propagation and time cost of pipeline frameworks. the pipeline framework brings more time complexity. consequently, it costs about 30 to 150 hours on 8 × 32gb gpus to train doc2edag, de-ppn, git, raat, or our ipgpf. thus, the training cost is the main limitation of our work.