Hierarchical Graph Attention for Spatial Document Visual Question Answering
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Solution Overview
Problem
Conventional Visual Question Answering (VQA) systems, particularly for Document VQA (DocVQA), face challenges in implementing robust spatial/visual, hierarchical, and semantic reasoning, leading to inadequate performance in extracting accurate answers from documents.
Innovation Solution
The proposed solution involves an enhanced DocVQA system that generates embeddings for document and query tokens based on physical layout and semantic aspects, constructs a hierarchical graph structure, and applies a Graph Attention Network (GAT) to locate the response within the document, while also employing data augmentation to improve robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional VQA systems use simple textual extraction methods, then the system complexity is reduced, but the ability to handle hierarchical and spatial reasoning is insufficient
Solution Approach 1:
The patent segments the document into a hierarchical graph structure with multiple levels (document level, section level, paragraph level, sentence level, token level). This segmentation allows the system to process complex documents by breaking them down into manageable units while preserving hierarchical relationships, thereby improving reasoning capability without requiring the entire system to handle all complexity at once.
Solution Approach 2:
The patent introduces a hierarchical dimension to the traditional flat text processing approach. By organizing tokens into sentences, paragraphs, sections, and document levels, the system adds a structural dimension that enables spatial and hierarchical reasoning. This dimensional transformation allows the system to reason about document structure and layout without significantly increasing computational complexity.
2Reliability
If the system processes entire documents at once, then the reasoning capability is improved, but the computational time and space requirements increase significantly
Solution Approach 1:
The patent divides the document processing into segmented operations at different hierarchical levels. Instead of processing the entire document as a single unit, the system processes tokens, sentences, paragraphs, and sections independently through the GAT, then aggregates results. This segmentation dramatically reduces the computational burden at each step while maintaining overall reasoning capability.
Solution Approach 2:
The patent applies partial action by processing only the relevant portions of the document at each hierarchical level. The GAT can focus attention on specific sections or paragraphs when answering particular questions, rather than processing the entire document uniformly. This selective processing reduces time and space requirements while maintaining reasoning accuracy.
3Measurement precision
If token-level 2D position embedding is used, then the layout information is captured, but the global view of document layout is insufficient
Solution Approach 1:
The patent segments layout information representation into multiple levels: token-level 2D position embeddings capture local layout details, while section-level and document-level embeddings capture global layout structure. This multi-level segmentation allows the system to simultaneously achieve precise local layout understanding and comprehensive global layout awareness.
Solution Approach 2:
The patent implements a nested structure where lower-level layout representations are contained within higher-level representations. Token-level position embeddings are nested within sentence-level embeddings, which are nested within paragraph-level embeddings, and so on. This nesting allows global layout information to encompass local details while maintaining both levels of precision.
4Productivity
If extractive approaches are used for answer extraction, then the extraction speed is improved, but the ability to answer questions with answers not conforming to pre-defined fields is reduced
Solution Approach 1:
The patent creates a universal representation system through the hierarchical graph and GAT that can handle multiple types of questions and answer formats. The same hierarchical structure and attention mechanism work for both pre-defined field extraction and open-ended questions requiring flexible answer generation, making the system multi-functional and adaptable to various query types.
Solution Approach 2:
The patent changes the representation parameters from fixed pre-defined fields to flexible hierarchical graph representations. By using the GAT to generate embeddings that capture semantic relationships and hierarchical structure, the system can adapt to any question format and generate appropriate answers without being constrained by pre-defined schemas, thereby improving versatility while maintaining processing efficiency.
Data Source
AI summary
Systems and methods for performing Document Visual Question Answering tasks are described. A document and query are received. The document encodes document tokens and the query encodes query tokens. The document is segmented into nested document sections, lines, and tokens. A nested structure of tokens is generated based on the segmented document. A feature vector for each token is generated. A graph structure is generated based on the nested structure of tokens. Each graph node corresponds to the query, a document section, a line, or a token. The node connections correspond to the nested structure. Each node is associated with the feature vector for the corresponding object. A graph attention network is employed to generate another embedding for each node. These embeddings are employed to identify a portion of the document that includes a response to the query. An indication of the identified portion of the document is be provided.