Document-level Event Extraction via Parallel Prediction Networks (2021)

Abstract: Document-level event extraction (DEE) is indispensable when events are described throughout a document. We argue that sentence-level extractors are ill-suited to the DEE task where event arguments always scatter across sentences and multiple events may co-exist in a document. It is a challenging task because it requires a holistic understanding of the document and an aggregated ability to assemble arguments across multiple sentences. In this paper, we propose an end-to-end model, which can extract structured events from a document in a parallel manner. Specifically, we first introduce a document-level encoder to obtain the document-aware representations. Then, a multi-granularity non-autoregressive decoder is used to generate events in parallel. Finally, to train the entire model, a matching loss function is proposed, which can bootstrap a global optimization. The empirical results on the widely used DEE dataset show that our approach significantly outperforms current state-of-the-art methods in the challenging DEE task. Code will be available at https://github.com/HangYang-NLP/DE-PPN .

## introduction the goal of event extraction (ee) is to identify events of a pre-specified type along with corresponding arguments from plain texts. a great number of previous studies @xcite @xcite @xcite @xcite @xcite @xcite focus on the sentence-level ee (see), while most of these works are based on the ace evaluation @xcite . 1 however, these see-based methods make predictions within 1 https://www.ldc.upenn.edu/ collaborations/past-projects/ace @xcite on november 1, 2018, shenzhen 007 co., ltd. received a notice that the corporate shareholder shanghai fukong co., ltd and the actual controller jing yan were judicial frozen. to handle these challenges in dee, previous works @xcite formulate dee as an event table filling task, i.e., filling candidate arguments into a predefined event table. specifically, they model the dee as a serial prediction paradigm, in which arguments are predicted in a predefined role order and multiple events are also extracted in predefined event order. such a manner is restricted to the extraction of individual arguments, and the former extraction will not consider the latter extraction results. as a result, errors will be propagated and the extraction performance is under satisfaction. in this paper, to avoid the shortage of serial prediction and tackle the aforementioned challenges in dee, we propose an end-to-end model, named document-to-events via parallel prediction networks (de-ppn). de-ppn is based on an encoder-decoder framework that can extract structured events from a whole document in a parallel manner. in detail, we first introduce a documentlevel encoder to obtain the document-aware representations. in such a way, a holistic understanding of the entire document is obtained. then, we leverage a multi-granularity decoder to generate events, which consists of two key parts: a role decoder and an event decoder. the role decoder is designed for handling the argument-scattering challenge, which can assemble arguments for an event based on document-aware representations. for addressing the challenge of multi-events effectively, an event decoder is designed to support generating multiple events. both of them are based on the non-autoregressive mechanism @xcite , which supports the extraction of multiple events in parallel. finally, for comparing extracted events to ground truths, we propose a matching loss function inspired by the hungarian algorithm @xcite . the proposed loss function can perform a global optimization by computing a bipartite matching between predicted and groundtruth events. in summary, our contributions are as follows: ## methodology before introducing our proposed approach for dee in this section, we first describe the task formalization of dee. formally, we denote t and r as the set of pre-defined event types and role categories, respectively. given an input document comprised of n s sentences @xmath0, the dee task aims to extract one or more structured events @xmath1, where each event y t i with event type t contains a series of roles (r 1 i , r 2 i , . . . , r n i ) filled by arguments (a 1 i , a 2 i , . . . , a n i ). k is the number of events contained in the document, n is the number of pre-defined roles for the event type t, @xmath2. the key idea of our proposed model, de-ppn, is that aggregate the document-level context to predict events in parallel. figure 2 illustrates the architecture of de-ppn, which consists of five key components: (1) candidate argument recognition, (2) document-level encoder, (3) multi-granularity decoder, (4) events prediction, and (5) matching loss function. event queries event role queries candidate arguments sentence position document sentences event type classification predicted event target event non-null null then a document-level encoder is designed to get the documentlevel representations. and a multi-granularity decoder is used to generate events in parallel based on documentaware representations. finally, the matching loss function can produce an optimal bipartite matching between predicted and ground-truth events, which bootstrap a global optimization. ## experiments and analysis in this section, we present empirical studies to answer the following questions: ## conclusion and future work in this paper, we propose an encoder-decoder model, de-ppn, to extract events in parallel from a document. for addressing the challenges (i.e., arguments-scattering and multi-events) in dee, we introduce a document-level encoder and a multigranularity decoder to generate events in parallel with document-aware representations. for training the parallel networks, we propose a matching loss function to perform a global optimization. experimental results show that de-ppn can significantly outperform sota methods especially facing the specific challenges in dee.