RMPL: Relation-aware Multi-task Progressive Learning with Stage-wise Training for Multimedia Event Extraction
arXiv:2602.13748v1 Announce Type: new Abstract: Multimedia Event Extraction (MEE) aims to identify events and their arguments from documents that contain both text and images. It requires grounding event semantics across different modalities. Progress in MEE is limited by the lack of annotated training data. M2E2 is the only established benchmark, but it provides annotations only for evaluation. This makes direct supervised training impractical. Existing methods mainly rely on cross-modal alignment or inference-time prompting with Vision--Language Models (VLMs). These approaches do not explicitly learn structured event representations and often produce weak argument grounding in multimodal settings. To address these limitations, we propose RMPL, a Relation-aware Multi-task Progressive Learning framework for MEE under low-resource conditions. RMPL incorporates heterogeneous supervision from unimodal event extraction and multimedia relation extraction with stage-wise training. The model
arXiv:2602.13748v1 Announce Type: new Abstract: Multimedia Event Extraction (MEE) aims to identify events and their arguments from documents that contain both text and images. It requires grounding event semantics across different modalities. Progress in MEE is limited by the lack of annotated training data. M2E2 is the only established benchmark, but it provides annotations only for evaluation. This makes direct supervised training impractical. Existing methods mainly rely on cross-modal alignment or inference-time prompting with Vision--Language Models (VLMs). These approaches do not explicitly learn structured event representations and often produce weak argument grounding in multimodal settings. To address these limitations, we propose RMPL, a Relation-aware Multi-task Progressive Learning framework for MEE under low-resource conditions. RMPL incorporates heterogeneous supervision from unimodal event extraction and multimedia relation extraction with stage-wise training. The model is first trained with a unified schema to learn shared event-centric representations across modalities. It is then fine-tuned for event mention identification and argument role extraction using mixed textual and visual data. Experiments on the M2E2 benchmark with multiple VLMs show consistent improvements across different modality settings.
Executive Summary
The article introduces RMPL, a Relation-aware Multi-task Progressive Learning framework designed to enhance Multimedia Event Extraction (MEE). MEE involves identifying events and their arguments from documents containing both text and images, a task complicated by the scarcity of annotated training data. The M2E2 benchmark, while established, only provides annotations for evaluation, making direct supervised training challenging. Existing methods often rely on cross-modal alignment or inference-time prompting with Vision-Language Models (VLMs), which can result in weak argument grounding. RMPL addresses these issues by incorporating heterogeneous supervision from unimodal event extraction and multimedia relation extraction through a stage-wise training process. Initial training focuses on learning shared event-centric representations across modalities, followed by fine-tuning for event mention identification and argument role extraction. Experiments on the M2E2 benchmark demonstrate consistent improvements across various modality settings.
Key Points
- ▸ RMPL is designed to improve Multimedia Event Extraction (MEE) by addressing the scarcity of annotated training data.
- ▸ The framework incorporates heterogeneous supervision from unimodal event extraction and multimedia relation extraction.
- ▸ Stage-wise training is employed to first learn shared event-centric representations and then fine-tune for specific tasks.
- ▸ Experiments on the M2E2 benchmark show consistent improvements across different modality settings.
Merits
Innovative Framework
RMPL introduces a novel approach to MEE by combining relation-aware multi-task learning with stage-wise training, which significantly enhances the extraction of event-centric representations from multimodal data.
Effective Use of Limited Data
The framework effectively leverages heterogeneous supervision to mitigate the limitations posed by the scarcity of annotated training data, making it a valuable tool in low-resource conditions.
Consistent Performance Improvements
Experiments on the M2E2 benchmark demonstrate consistent performance improvements across various modality settings, highlighting the robustness and effectiveness of the RMPL framework.
Demerits
Dependency on VLMs
While RMPL shows promise, its reliance on Vision-Language Models (VLMs) for inference-time prompting may still result in weak argument grounding in certain multimodal settings.
Limited Benchmark Availability
The lack of additional benchmarks beyond M2E2 limits the scope of validation and may restrict the generalizability of the findings.
Complexity of Implementation
The stage-wise training process, while effective, adds complexity to the implementation and may require significant computational resources.
Expert Commentary
The article presents a significant advancement in the field of Multimedia Event Extraction (MEE) by introducing the RMPL framework, which addresses critical challenges related to data scarcity and multimodal integration. The innovative use of relation-aware multi-task learning and stage-wise training demonstrates a robust approach to improving event-centric representations. However, the reliance on Vision-Language Models (VLMs) and the complexity of implementation pose notable limitations. The consistent performance improvements observed in experiments on the M2E2 benchmark underscore the potential of RMPL, but further validation with additional benchmarks is necessary to ensure generalizability. The practical implications of this research are substantial, particularly in areas requiring real-time analysis of multimodal content. Additionally, the policy implications related to data privacy and ethical AI use are noteworthy, as advanced MEE tools can significantly impact regulatory frameworks and content moderation practices. Overall, the article contributes valuable insights to the field and sets a strong foundation for future research in multimodal event extraction.
Recommendations
- ✓ Further validation of the RMPL framework using additional benchmarks beyond M2E2 to ensure the generalizability of the findings.
- ✓ Exploration of alternative approaches to mitigate the dependency on VLMs, potentially enhancing argument grounding in multimodal settings.