Code-MIE: A Code-style Model for Multimodal Information Extraction with Scene Graph and Entity Attribute Knowledge Enhancement
arXiv:2603.20781v1 Announce Type: new Abstract: With the rapid development of large language models (LLMs), more and more researchers have paid attention to information extraction based on LLMs. However, there are still some spaces to improve in the existing related methods. First, existing multimodal information extraction (MIE) methods usually employ natural language templates as the input and output of LLMs, which mismatch with the characteristics of information tasks that mostly include structured information such as entities and relations. Second, although a few methods have adopted structured and more IE-friendly code-style templates, they just explored their methods on text-only IE rather than multimodal IE. Moreover, their methods are more complex in design, requiring separate templates to be designed for each task. In this paper, we propose a Code-style Multimodal Information Extraction framework (Code-MIE) which formalizes MIE as unified code understanding and generation. Co
arXiv:2603.20781v1 Announce Type: new Abstract: With the rapid development of large language models (LLMs), more and more researchers have paid attention to information extraction based on LLMs. However, there are still some spaces to improve in the existing related methods. First, existing multimodal information extraction (MIE) methods usually employ natural language templates as the input and output of LLMs, which mismatch with the characteristics of information tasks that mostly include structured information such as entities and relations. Second, although a few methods have adopted structured and more IE-friendly code-style templates, they just explored their methods on text-only IE rather than multimodal IE. Moreover, their methods are more complex in design, requiring separate templates to be designed for each task. In this paper, we propose a Code-style Multimodal Information Extraction framework (Code-MIE) which formalizes MIE as unified code understanding and generation. Code-MIE has the following novel designs: (1) Entity attributes such as gender, affiliation are extracted from the text to guide the model to understand the context and role of entities. (2) Images are converted into scene graphs and visual features to incorporate rich visual information into the model. (3) The input template is constructed as a Python function, where entity attributes, scene graphs and raw text compose of the function parameters. In contrast, the output template is formalized as Python dictionaries containing all extraction results such as entities, relations, etc. To evaluate Code-MIE, we conducted extensive experiments on the M$^3$D, Twitter-15, Twitter-17, and MNRE datasets. The results show that our method achieves state-of-the-art performance compared to six competing baseline models, with 61.03\% and 60.49\% on the English and Chinese datasets of M$^3$D, and 76.04\%, 88.07\%, and 73.94\% on the other three datasets.
Executive Summary
Code-MIE, a novel framework for multimodal information extraction, presents a unified code understanding and generation approach to tackle the limitations of existing methods. By incorporating structured code-style templates, entity attributes, and scene graphs, Code-MIE achieves state-of-the-art performance on various datasets. The framework's design allows for efficient and accurate extraction of entities, relations, and other relevant information from multimodal sources. The results demonstrate the effectiveness of Code-MIE in handling complex information tasks, particularly in the context of scene graph and entity attribute knowledge enhancement. This framework has the potential to revolutionize information extraction and opens up new avenues for research in this area.
Key Points
- ▸ Code-MIE formalizes MIE as unified code understanding and generation
- ▸ Incorporates structured code-style templates, entity attributes, and scene graphs
- ▸ Achieves state-of-the-art performance on various datasets
Merits
Strength in Handling Multimodal Data
Code-MIE effectively incorporates structured code-style templates, entity attributes, and scene graphs to handle complex multimodal information extraction tasks.
Efficient and Accurate Information Extraction
The framework's design enables efficient and accurate extraction of entities, relations, and other relevant information from multimodal sources.
Potential for Real-World Applications
Code-MIE has the potential to revolutionize information extraction and opens up new avenues for research in this area, particularly in industries such as healthcare, finance, and social media.
Demerits
Limited Evaluation on Specific Domains
The evaluation of Code-MIE is limited to four specific datasets, and its performance on other domains and languages remains to be explored.
Potential Overreliance on Scene Graphs
The framework's reliance on scene graphs may lead to overfitting or biased results if the scene graphs are not accurately constructed or if the data is not representative of the target domain.
Scalability and Interpretability Concerns
As the size and complexity of the datasets increase, Code-MIE may face scalability and interpretability challenges, which need to be addressed through further research and development.
Expert Commentary
Code-MIE presents a significant advancement in the field of information extraction, particularly in the context of multimodal data. The framework's design and evaluation demonstrate its effectiveness in handling complex information tasks and achieving state-of-the-art performance on various datasets. However, the evaluation is limited to specific domains and languages, and further research is required to explore its performance on other domains and languages. Additionally, the framework's reliance on scene graphs and scalability concerns need to be addressed through further research and development. Overall, Code-MIE has the potential to revolutionize information extraction and opens up new avenues for research in this area.
Recommendations
- ✓ Further exploration of Code-MIE's performance on other domains and languages is recommended to validate its effectiveness in various contexts.
- ✓ Addressing the scalability and interpretability concerns of Code-MIE through further research and development is essential to ensure its practical applications.
Sources
Original: arXiv - cs.CL
