Cross-media Structured Common Space for Multimedia Event Extraction
Manling Li, Alireza Zareian, Qi Zeng, Spencer Whitehead, Di Lu, Heng Ji, Shih‐Fu Chang · 2020
We introduce a new task, MultiMedia Event Extraction (M 2 E 2 ), which aims to extract events and their arguments from multimedia documents.We develop the first benchmark and collect a dataset of 245 multimedia news articles with extensively annotated events and arguments.1 We propose a novel method, Weakly Aligned Structured Embedding (WASE), that encodes structured representations of semantic information from textual and visual data into a common embedding space.The structures are aligned across modalities by employing a weakly supervised training strategy, which enables exploiting available resources without explicit cross-media annotation.Compared to unimodal state-of-the-art methods, our approach achieves 4.0% and 9.8% absolute F-score gains on text event argument role labeling and visual event extraction.Compared to stateof-the-art multimedia unstructured representations, we achieve 8.3% and 5.0% absolute Fscore gains on multimedia event extraction and argument role labeling, respectively.By utilizing images, we extract 21.4% more event mentions than traditional text-only methods.