FinEmbedDiff: A Cost-Effective Approach of Classifying Financial Documents with Vector Sampling using Multi-modal Embedding Models
Anjanava Biswas, Wrick Talukdar·May 28, 2024
Summary
The paper "FINEMBEDDIFF: A Cost-Effective Approach of Classifying Financial Documents with Vector Sampling using Multi-Modal Embedding Models" introduces a novel method for efficiently classifying multi-modal financial documents by combining pre-trained text and visual embedding models like CLIP, VisualBERT, and LXMERT. It reduces computational costs through vector sampling, demonstrating strong generalization across diverse document types and domains. The approach, which outperforms text-only and existing multi-modal baselines, uses cosine similarity or L2 distance to classify unseen documents based on their embeddings. The study evaluates the method on a large-scale financial dataset, showing competitive accuracy and practical applicability in real-world scenarios. Future research potential includes domain-specific improvements, multi-task learning, and explainable AI for enhanced financial decision-making.
Introduction
Background
Evolution of financial document analysis
Importance of multi-modal approaches in finance
Objective
To develop a cost-effective method for document classification
Improve efficiency and accuracy in financial document understanding
Method
Data Collection
Source of multi-modal financial documents
Data preprocessing techniques (e.g., cleaning, annotation)
Vector Sampling Technique
Selection of pre-trained models (CLIP, VisualBERT, LXMERT)
Sampling strategy for efficient representation
Multi-Modal Embedding
Fusion of text and visual embeddings
Techniques for combining information (cosine similarity, L2 distance)
Model Training and Evaluation
Training methodology
Performance metrics (accuracy, efficiency)
Comparison with text-only and baseline models
Experiments and Results
Dataset Description
Scale and diversity of the financial dataset
Performance Analysis
Accuracy on unseen documents
Computational cost reduction
Generalization across Domains
Cross-domain classification results
Baseline Comparisons
Outperformance of FINEMBEDDIFF
Applications and Future Research
Practical Use Cases
Real-world scenarios and potential benefits
Research Directions
Domain-specific model adaptation
Multi-task learning for enhanced performance
Explainable AI for financial decision-making
Conclusion
Summary of key findings
Limitations and implications
Contributions to the field of financial document classification
Basic info
papers
information retrieval
artificial intelligence
Advanced features
Insights
Which pre-trained models are combined in the proposed method?
How does the approach in the paper address computational costs?
What evaluation is conducted on the large-scale financial dataset, and what are the results?
What method does the paper "FINEMBEDDIFF" propose for classifying financial documents?