Two-Stream Modeling for Document-Level Event Argument Extraction Using Contextual Clue and AMR Structures

Document-level (Doc-level) event argument extraction (EAE) needs to deal with longer text inputs and complex semantic relationships than sentence-level, making it a challenging information extraction task. Extracting event arguments from an entire document primarily faces two critical issues: (i) how to handle the long-distance dependency between trigger and role arguments and (ii) how to extract key event contextual information. We propose a two-stream modeling framework using contextual clues and abstract meaning representation (AMR) parsing (TSCA). TSCA employs two-stream encoding to semantically model the document from event-critical context and event-semantic structure two perspectives. This approach leverages both contextual clues and semantic structure information to better mitigate the two issues. We incorporate AMR to assist in the semantic understanding of complex event structures and effectively capture long-distance dependencies. Additionally, we introduce a span indicator based on triggers to adaptively merge the two-stream information, enhancing the capture of semantic relevance between triggers and candidate arguments. We validated the effectiveness of our method on the public datasets RAMs and Wikievents, where TSCA achieved the best scores in various subtasks, surpassing state-of-the-art models by 3.02 F1 and 1.01 F1, respectively.