Reinforcement Document Transformer for Hierarchical Multimodal QA
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Solution Overview
Problem
Existing question answering (QA) systems face challenges in handling documents with varied formats, multiple classes, and complex structures, particularly in domains like insurance and healthcare, where answers often span multiple text spans and require accurate context correlation across headings and paragraphs.
Innovation Solution
A Reinforcement Document Transformer (RDT) system that utilizes document template structures, domain ontologies, and hierarchical content storage to analyze multimodal queries, determining semantic relationships and generating relevant answers by leveraging natural language processing, computer vision, and machine learning techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing QA systems process documents with varied formats and complex structures, then they can handle diverse document types, but the accuracy of answering queries spanning multiple text spans deteriorates
Solution Approach 1:
The document is segmented into multiple text spans with hierarchical structure (headings, paragraphs, sections). Each span is processed independently to identify relevant portions, then results are correlated to form comprehensive answers. This segmentation allows the system to handle diverse document formats while maintaining answer accuracy by focusing on specific relevant sections rather than treating the entire document as a single unit.
Solution Approach 2:
The system introduces a hierarchical dimension to document processing by organizing text spans across multiple levels (document → section → paragraph → sentence). This multi-dimensional structure enables accurate correlation of context across headings and paragraphs while accommodating varied document formats, resolving the contradiction between versatility and precision.
2Measurement precision
If QA systems correlate context across headings and paragraphs, then answer accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary processing of document structures before query answering, pre-organizing text spans into hierarchical segments and pre-computing relevant metadata. This preliminary action enables faster context correlation during actual query processing, as the structural framework is already established and ready for efficient information retrieval.
3Measurement precision
If the system analyzes document layouts and structures, then retrieval accuracy improves, but system complexity increases
Solution Approach 1:
The system extracts and separates the structural analysis component from the overall processing pipeline. Document layout and structure analysis is performed as a distinct preliminary step that outputs structured metadata, which then guides the retrieval process. This extraction reduces system complexity by modularizing the analysis function while maintaining high retrieval accuracy through dedicated structural processing.
Data Source
AI summary
A computer-implemented method and system for enrichment of responses in a multimodal conversation environment are disclosed. A Question Answer (QA) engine, such as a reinforcement document transformer exploits a document template structure or layout, adapts the information extraction using a domain ontology, stores the enriched contents in a hierarchical form, and learns context and query patterns based on the intent and utterances of one or more queries. The region of enriched content for preparing a response to a given query is expanded or collapsed by navigating upwards or downwards in the hierarchy. The QA engine returns the most relevant answer with the proper context for one or more questions. The responses are provided to the user in one or more modalities.