\n ## Screenshot: Document-Level Event Argument Extraction Example ### Overview The image is a screenshot of a document demonstrating an example of Document-Level Event Argument Extraction. It shows a "Prompt" section with instructions and input text, and an "Output" section displaying the extracted result. The screenshot is framed by a dashed border. ### Components/Axes The screenshot is divided into three main sections: 1. **Header:** Contains the text "Example: Document-Level Event Augment Extraction" and "ID: wiki&deae&scenario_en_kairos_44&02". This is positioned at the top-center of the image. 2. **Prompt Section:** Labeled "Prompt", this section contains the instructions and input text. It is positioned in the upper-left portion of the image. 3. **Output Section:** Labeled "Output", this section displays the extracted output. It is positioned in the bottom-left portion of the image. ### Content Details **Header Text:** "Example: Document-Level Event Augment Extraction" "ID: wiki&deae&scenario_en_kairos_44&02" **Prompt Text:** "Instruction: As an expert in Document-level Event Argument Extraction, your task is to produce a single sentence..." "Input: WACO, TX U.S. Attorney John E. Murphy and FBI Special Agent in Charge Cory B. Nelson announced that a federal grand jury seated in Waco returned...The template is <arg1> arrested or jailed <arg2> for <arg3> at <arg4>." **Output Text:** "Officers arrested or jailed Abdo for <arg3> at <arg4>." ### Key Observations The example demonstrates how a template with argument placeholders (<arg1>, <arg2>, <arg3>, <arg4>) is populated with extracted information from the input text. The output sentence fills in "Officers" for <arg1> and "Abdo" for <arg2>, while leaving <arg3> and <arg4> as placeholders. ### Interpretation This screenshot illustrates a natural language processing (NLP) task focused on event argument extraction. The goal is to identify key elements within a text (the arguments) and map them to predefined roles within a template. The example shows a simplified scenario where the system successfully identifies the agents involved in an arrest or jailing event. The remaining placeholders suggest that further processing or information is needed to complete the sentence. The "ID" suggests this is part of a larger dataset or experiment ("wiki&deae&scenario_en_kairos_44&02"). The use of "kairos" in the ID might indicate a focus on timeliness or opportune moments in event extraction.