Cross-lingual Document Analysis for Energy Industry
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Solution Overview
Problem
Existing multilingual multi-modal AI tools face challenges in accurately analyzing and interpreting domain-specific documents, such as those in the energy industry, due to limitations in understanding energy-related terminology, complex diagrams, and specialized formatting.
Innovation Solution
An AI-driven document analysis system is developed that generates cross-lingual mixed documents to retrain models like LayoutXLM, enabling improved cross-lingual comprehension and translation of energy industry domain-specific documents by leveraging multilingual training data and utilizing methods like tag-based and paragraph-based text content extraction and replacement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing multilingual multi-modal AI tools are used to analyze domain-specific documents, then basic translation capability is provided, but accuracy in understanding domain-specific terminology and content is insufficient
Solution Approach 1:
The system performs preliminary actions by generating cross-lingual mixed documents from parallel documents in different languages before the actual translation task. These mixed documents serve as pre-trained data that equip the AI model with cross-lingual comprehension capabilities, enabling it to better understand domain-specific terminology and content in subsequent translation tasks.
Solution Approach 2:
The system changes the training parameters of the pre-trained machine learning model by retraining it with cross-lingual mixed documents. This parameter change transforms the model from a general-purpose translator to one specialized in cross-lingual document analysis, improving both translation accuracy and domain-specific understanding.
2Loss of information
If users read and analyze large and complex documents directly, then complete understanding is pursued, but time consumption and reading difficulty increase significantly
Solution Approach 1:
The system extracts key information from large and complex documents by translating them into the user's native language using the retrained AI model. This extraction process separates essential information from the original document structure, presenting it in a more accessible format that reduces reading time while maintaining comprehensive understanding.
Solution Approach 2:
The system introduces an intermediary translation layer between the original document and the user. The AI-driven translation system acts as a mediator that bridges language barriers and domain-specific knowledge gaps, allowing users to understand document content without directly reading the original complex text.
3Measurement precision
If domain-specific knowledge is required to understand specialized documents, then accurate interpretation is achieved, but user accessibility and ease of operation decrease
Solution Approach 1:
The system achieves universality by creating a translation model that handles multiple languages and domain-specific contents through a single retrained AI model. This multi-functional model provides accurate interpretation across different languages and domains without requiring users to possess specialized knowledge in each area.
Solution Approach 2:
The system enables self-service by automatically translating and adapting domain-specific documents into user-friendly formats without requiring users to have expert knowledge. The AI model independently handles the complexity of domain-specific terminology and provides accessible translations, allowing users to understand specialized content without becoming experts themselves.
Data Source
AI summary
Systems and methods for providing a cross-lingual PDF analysis and translation system specifically designed for the energy industry. The cross-lingual PDF analysis and translation system uses various methods (e.g., tag-based or paragraph-based document extraction and mixing) to create a cross-lingual mixed document dataset by modifying HTML files from multilingual web pages. The cross-lingual mixed document dataset is used to retrain a LayoutXLM model, enabling the LayoutXLM model to further build cross-lingual relations by evaluating the model on various form understanding benchmarks.