Summary of Mmutf: Multimodal Multimedia Event Argument Extraction with Unified Template Filling, by Philipp Seeberger et al.
MMUTF: Multimodal Multimedia Event Argument Extraction with Unified Template Filling
by Philipp Seeberger, Dominik Wagner, Korbinian Riedhammer
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the challenge of Multimedia Event Extraction (MEE), a crucial task in today’s multimedia-driven world. Recent methods employ weak alignment strategies and data augmentation with simple classification models, neglecting the capabilities of natural language-formulated event templates for the challenging Event Argument Extraction (EAE) task. To bridge this gap, the authors propose a unified template filling model that connects textual and visual modalities via textual prompts, enabling cross-ontology transfer and incorporating event-specific semantics. Experimental results on the M2E2 benchmark demonstrate the effectiveness of this approach, surpassing the current state-of-the-art (SOTA) by +7% F1 for textual EAE and performing better than the second-best systems for multimedia EAE. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making it easier to understand events in videos and text. Right now, computers are not very good at doing this because they don’t use natural language templates that humans would use. The authors of this paper have come up with a new way to do event extraction by using textual prompts to connect video and text together. This helps the computer learn more about events and make better predictions. The results show that their method is much better than others at understanding events in both videos and text. |
Keywords
* Artificial intelligence * Alignment * Classification * Data augmentation * Semantics
