# DEEDP: Document-Level Event Extraction Model Incorporating Dependency Paths
## 1 Introduction  
Event extraction (EE) is a crucial and challenging task for Information Extraction (IE), which aims to identify events of pre-defined types and extract arguments to fill the corresponding roles from plain texts. In recent years, EE has received growing attention in many domains, such as Finance, Public Safety, Intelligent Operations, and Maintenance, because it can produce valuable structured event knowledge to facilitate critical incident handling in these domains. Most existing approaches [1; 2; 3; 4; 5; 6] mainly explore sentence-level EE (SEE), which detects and extracts events from a single sentence within the given document. Moreover, the evaluation work of these approaches is mainly based on a manually annotated benchmark, ACE-2005 [7], which labels only event arguments within a sentence scope. However, there may be more than one event described in a real-world text, and the arguments for an event record may distribute in multiple sentences. Therefore, the SEE performs poorly when extracting events across sentences. To this end, document-level event extraction (DEE) is proposed to address the aforementioned problems. Compared with SEE, DEE focuses on two vital challenging tasks: arguments-scattering and multi-events. Arguments-scattering refers to the arguments of an event that may be distributed in different sentences in a document. As illustrated in Figure 1, the arguments of Equity Overweight (EO) scatter across four sentences ([S5] to [S8]), and the extraction cannot be completed within a single sentence. In this scenario, the DEE model needs to understandthe whole document comprehensively and capture long-distance interaction information among arguments distributed in different sentences. Multi-events indicates that a document may describe several correlated events. The two events, Equity Underweight (EU) and EO, are interdependent and there is no obvious textual boundary between them. To tackle this challenge, DEE models need to integrate local and global information, fill the event roles of a pre-defined type with relevant arguments that scatter across sentences, and identify multiple events simultaneously. However, in real-world applications, these challenges are often coupled, which makes the DEE task more difficult and complicated. To address the two aforementioned challenges, previous studies [8; 9; 10; 11; 12; 13] employed a multi-Transformer [14] or Long Short-Term Memory (LSTM) [15] encoder to obtain sentence-level and document-level representations for entities and sentences, ignoring the feature loss problem while performing mean pooling and max pooling operations, and without the ability to model long-distance dependencies between arguments. [PERSON] et al. [16] noted the latter issue, and proposed the application of a multi-turn and multi-granularity Transformer encoder to model local and global interaction, which can capture dependencies between arguments and help to understand the entire document comprehensively. Although this approach is promising, it overlooks the feature loss problem, and the input token features they used are limited. In this work, to overcome the aforementioned shortcomings of previous methods and address the troublesome challenges in DEE, we propose a novel end-to-end model, the Document-level Event Extraction Model Incorporating Dependency Paths (DEEDP). DEEDP introduces a novel multi-granularity encoder framework that can model long-distance dependencies and capture more sentence-level syntactic features to facilitate DEE. In detail, we first designed a Transformer-based encoder, Transformer-M, by adding a Syntactic Feature Attention mechanism to the Transformer, which can capture more interdependency information between input tokens and help enhance the semantics for sentence-level representations of entities. We then stacked Transformer-M and Transformer to integrate sentence-level and document-level features; we thus obtained semantic enhanced document-aware representations for each entity and modeled long-distance dependencies between arguments.