From Simple to Complex: A Progressive Framework for Document-level Informative Argument Extraction (2023)

Abstract: Document-level Event Argument Extraction (EAE) requires the model to extract arguments of multiple events from a single document. Considering the underlying dependencies between these events, recent efforts leverage the idea of “memory”, where the results of already predicted events are cached and can be retrieved to help the prediction of upcoming events. These methods extract events according to their appearance order in the document, however, the event that appears in the first sentence does not mean that it is the easiest to extract. Existing methods might introduce noise to the extraction of upcoming events if they rely on an incorrect prediction of previous events. In order to provide more reliable memory, we propose a simple-to-complex progressive framework for document-level EAE. Specifically, we first calculate the difficulty of each event and then, we conduct the extraction following a simple-to-complex order. In this way, the memory will store the most certain results, and the model could use these reliable sources to help the prediction of more difficult events. Experiments on WikiEvents show that our model outperforms SOTA by 1.4% in F1, indicating the proposed simple-to-complex framework is useful in the EAE task.

## introduction document-level event argument extraction (eae) aims at identifying the participants of multiple events from a document and classifying them into proper roles @xcite @xcite @xcite . understanding events in documents is crucial for a line of downstream tasks, such as machine reading comprehension @xcite and dialogue systems @xcite . ## task definition in this work, we focus on document-level informative argument extraction foot_0 (iae) @xcite , where informative arguments are far more distant than local/uninformative ones and provide more useful information about an event. we formulate document-level iae as a generative template-filling task following @xcite and @xcite . given a document d with triggers marked (using a special @xmath0, our goal is to extract all the arguments of e to fill in the slots of the event template t . each event consists of (1) an event trigger, which has a specific type e (we use e to represent an event); (2) a series of event arguments, each corresponding to a specific role. in the event ontology, event types and argument roles are pre-defined, and event templates depicting the relationship between the argument roles of an event are also provided. for example, the template for @xmath1: <@xmath2. we replace all @xmath3. if the model extracts an argument, @xmath4. if no, the @xmath5. ## method in this section, we illustrate our framework based on simple-to-complex prediction (figure 1 ). first, we introduce our memory-enhanced iae model (section 3.1). here, we use retrieval to augment model input and apply constrained decoding to improve model output, both leveraging inter-event dependencies to benefit prediction. second, we elaborate on how to define and calculate the difficulty of an event, and how to reorder events in a document from simple to complex for simple-tocomplex prediction (section 3.2). ## conclusion in this work, we propose the idea of simple-tocomplex prediction for events in a document, where events in a document are reordered from simple to complex and predicted accordingly. besides, we introduce retrieval to augment model input and apply constrained decoding to improve model output. empirical results and analysis demonstrate that our best model outperforms prior methods by a notable margin and our simple-to-complex prediction is beneficial since it allows more inter-event dependency links, i.e., from an event to all the events appeared after it.