Good Visual Guidance Makes A Better Extractor: Hierarchical Visual Prefix for Multimodal Entity and Relation Extraction (2205.03521v1)

Abstract: Multimodal named entity recognition and relation extraction (MNER and MRE) is a fundamental and crucial branch in information extraction. However, existing approaches for MNER and MRE usually suffer from error sensitivity when irrelevant object images incorporated in texts. To deal with these issues, we propose a novel Hierarchical Visual Prefix fusion NeTwork (HVPNeT) for visual-enhanced entity and relation extraction, aiming to achieve more effective and robust performance. Specifically, we regard visual representation as pluggable visual prefix to guide the textual representation for error insensitive forecasting decision. We further propose a dynamic gated aggregation strategy to achieve hierarchical multi-scaled visual features as visual prefix for fusion. Extensive experiments on three benchmark datasets demonstrate the effectiveness of our method, and achieve state-of-the-art performance. Code is available in https://github.com/zjunlp/HVPNeT.

• The paper presents HVPNeT, a new framework that integrates hierarchical visual prefixes into BERT to improve multimodal entity and relation extraction.
• It employs dynamic gated aggregation to selectively weight visual features, effectively reducing noise from irrelevant visual data.
• Experimental results on Twitter-2015, Twitter-2017, and MNRE datasets demonstrate state-of-the-art F1 score improvements and robust cross-modality interaction.

Hierarchical Visual Prefix for Multimodal Entity and Relation Extraction

The paper "Good Visual Guidance Makes A Better Extractor: Hierarchical Visual Prefix for Multimodal Entity and Relation Extraction" introduces a novel Hierarchical Visual Prefix fusion NeTwork (HVPNeT) for enhancing the extraction of named entities and their relations from textual data augmented with visual information. This framework is developed to address prevalent challenges in multimodal named entity recognition (MNER) and relation extraction (MRE), particularly the sensitivity to irrelevant visual elements that can impair performance.

The central proposition of HVPNeT is the use of a pluggable visual prefix that integrates hierarchical visual features into text representations. This is achieved by prepending visual representations as a prefix to the text data at each self-attention layer within the BERT architecture. This innovative approach aims to enhance the robustness and effectiveness of MNER and MRE tasks, especially in scenarios where visual distractors could impede model accuracy.

Key Methodological Innovations

1. Hierarchical Visual Prefix Integration: HVPNeT integrates visual representations at each self-attention layer as a "prefix," allowing these additional inputs to guide the model's attention mechanism more effectively. This hierarchical approach utilizes multi-scale visual features, grounded in the idea that visual information naturally precedes textual descriptions in multimodal data.
2. Dynamic Gated Aggregation: The model employs a dynamic gated aggregation strategy to facilitate the selection of pertinent visual features based on their relevance and hierarchical nature. This strategy dynamically weights visual features, ensuring that the model harnesses the most contextually appropriate information.
3. Robust Cross-Modality Interaction: By treating visual information as a prompt, HVPNeT seeks to mitigate the effect of irrelevant visual data, consequently improving the model's error resilience.

Experimental Evaluation

The effectiveness of HVPNeT was validated through extensive experimentation on three benchmark datasets, demonstrating state-of-the-art performance improvements across both MNER and MRE tasks. Notably, HVPNeT achieved significant gains in F1 scores compared to existing models, highlighting the superiority of integrating hierarchical visual data.

• The model outperformed its predecessors in datasets such as Twitter-2015, Twitter-2017, and MNRE, exhibiting a notable increase in accuracy in scenarios where irrelevant visual objects are present.
• The performance under cross-task scenarios further substantiated the model's adaptability and its capacity to leverage multimodal data effectively, even when transferring learned representations between distinct tasks.

Implications and Future Directions

The introduction of HVPNeT presents both theoretical and practical implications. Theoretically, it challenges existing paradigms of multimodal data fusion by emphasizing the sequential and hierarchical integration of visual clues. Practically, it proposes a more resilient approach to MNER and MRE tasks, enhancing the reliability of automated extraction systems within noisy, multimodal environments typical of social media and other digital platforms.

Future research directions could explore extending the hierarchical prefix framework to pretraining regimes across broader LLMs, potentially elevating cross-modal interactions in large-scale datasets. Additionally, adapting the reverse methodology—using textual data to improve visual tasks—poses an intriguing avenue for expanding this conceptual framework.

In summary, the paper meticulously details a novel architecture that enriches text processing with visual context, offering promising advancements in concrete applications where multimodal data fusion is critical.