# Document-Level Event Role Filler Extraction Using Key-Value Memory Network
## 1 Introduction  
In current practical use, it is unrealistic to extract complete arguments from a sentence alone. Therefore, the main task now is to improve the accuracy of document-level event element extraction. A complete document-level event extraction typically includes event detection, role-filler extraction, and event tracking. Our main task is to study the document-level extraction of event arguments. In Figure 1, the left side shows multiple sentences from an article, and some arguments are extracted from the left side of the article. For example, in an article containing an attack event, this attack event will have some event actors (e.g., the individual attacker, attacker organization, attack target, victim, weapon, etc.). Our main goal is to identify the textual scope of each event metric in the text, and thus an accurate capture of multi-sentence contextual information is required. In this example, the victim is mentioned implicitly in the first sentence, however, is only explicitly referred to in the third sentence. This event involves a total of three sentences, which shows that solving the problem of crossing sentence boundaries and performing document-level event extraction is important to facilitate downstream tasks. Back in 2009, document-level event arguments were approached as a template-generation problem, and a new model was proposed. The model first identifies the type of event in a sentence. Then, the corresponding event roles are identified based on the event types. This model is called the pipeline model [1, 2]. However, all models have certain problems: (1) they require the help of many external features, such as some syntactic features, which are more useful as an aid to document-level event extraction, and lexical features, which facilitate the identification of individual event arguments; and (2) pipeline models suffer from error propagation. Previous studies have shown that end-to-end neural sequence models perform well in tasks, such as NER [3] and sentence-level event extraction on the ACE dataset [4]. They are more suitable for downstream tasks involving individual sentences. The neural network model works well for sentence-level event extraction. However, it still faces many difficulties in document-level event extraction. The arguments of an event usually run through the whole text, and developing a method to establish long-term dependencies across multiple sentences [5] is one of the greatest difficulties encountered thus far. The RNN back-propagation algorithm [4] is typically used to model long sentences. In 2020, [PERSON] et al. [6] applied the end-to-end neural sequence model as a new framework for document-level event extraction, which addressed the impact of long sentences on model performance to an extent. End-to-end neural sequence models usually use BiLSTM to update parameters; however, the inputs of BiLSTM are individual word vectors, which cannot consider the contextual information and, thus, cannot capture the contextual information of the whole chapter. [PERSON] et al. [7] attempted to solve this problem by dynamically aggregating the embedded contextual information of each word. However, this does not work in cases of individual sentence input. To address the above issues, we propose a hierarchical contextual framework to enhance event theory meta-extraction modeling. The sentence-level representation mainly uses single sentences, which are input into the BERT-base to decode and obtain sentence-level vectors, and then the individual sentence vectors are passed through BiLSTM to obtain the corresponding hidden layer states. A key-value memory network is used at the document level, using sentence-level word vectors as K-values and hidden values as V. Each k-v slot value is updated within a certain time, and the obtained value and word-level representation are used as the k, v, and q of the attention mechanism to calculate the document-level representation.