Papers by Philipp Seeberger
MMUTF: Multimodal Multimedia Event Argument Extraction with Unified Template Filling (2024.findings-emnlp)
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| Challenge: | Recent MEE methods focus on weak alignment strategies and data augmentation with simple classification models. |
| Approach: | They propose a unified template filling model that connects textual and visual modalities via textual prompts. |
| Outcome: | The proposed model surpasses the current SOTA on textual EAE by +7% F1 and performs generally better than the second-best systems for multimedia EAE. |
Optimized Speculative Sampling for GPU Hardware Accelerators (2024.emnlp-main)
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| Challenge: | Large foundational speech and language models require more memory and computational resources to generate long sequences. |
| Approach: | They propose to optimize speculative sampling for parallel hardware accelerators by combining multiple GPU threads to reduce profiling time. |
| Outcome: | The proposed approach improves profiling time from 6% to 13% without compromising accuracy. |
Evaluation Pitfalls and Challenges in Multimedia Event Extraction (2026.acl-long)
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| Challenge: | Recent work has focused on textual content, but recent work has explored the integration of additional modalities to support more accurate and comprehensive event understanding. |
| Approach: | They propose to analyze the evaluation pitfalls of multimedia event extraction by combining textual and visual inputs to identify events and their arguments across multiple modalities. |
| Outcome: | The proposed model overestimates performance and performance of the proposed model in a series of controlled experiments under a strict evaluation framework. |