Tabular Question Answering With Attention-Based Semantic Reasoning
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Solution Overview
Problem
Conventional question answering systems are limited to simple exact matching and struggle with variable size tables, requiring specialized knowledge and being inefficient for large tables with semantic ambiguity.
Innovation Solution
A question answering apparatus using an attention-based mechanism with multiple attention heads and a neural programming model that computes probabilistic distributions over table operations, enabling robust and scalable question answering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional question answering systems use simple exact matching, then the system is easy to implement, but it cannot handle semantic ambiguity and variable size tables effectively
Solution Approach 1:
The patent replaces conventional mechanical exact matching mechanisms with a neural attention-based system. The attention network computes probabilistic distributions over table cells and operations using neural networks, enabling semantic understanding rather than rigid pattern matching. This substitution allows the system to handle semantic ambiguity and variable table structures while maintaining computational efficiency.
Solution Approach 2:
The patent changes the fundamental parameters of question answering from binary exact matching to probabilistic scoring. The attention mechanism produces continuous probability values for cell selections and operation executions, allowing the system to handle semantic variations and ambiguous queries. This parameter transformation enables flexible adaptation to different table structures and semantic interpretations.
2Productivity
If conventional systems are limited to simple exact matching, then the device complexity is low, but the productivity and scalability are limited
Solution Approach 1:
The patent segments the question answering process into distinct functional components: query encoding, attention computation, cell probability calculation, operation selection, and answer generation. Each component handles a specific aspect of the reasoning process, enabling modular implementation and improved scalability. The segmentation allows efficient processing of large tables by dividing the complex task into manageable computational steps.
Solution Approach 2:
The attention-based system provides universal functionality for handling various table structures and query types through a single unified architecture. The same attention mechanism can process different table sizes, handle semantic ambiguities, and execute multiple operations (selection, filtering, aggregation) without requiring separate specialized systems. This multi-functionality significantly improves productivity and scalability.
3Measurement precision
If the system uses probabilistic computation with multiple attention heads, then the accuracy and flexibility improve, but the computational time and complexity increase
Solution Approach 1:
The patent applies partial action by computing attention probabilities for only the most relevant table cells and operations rather than exhaustively evaluating all possibilities. The attention mechanism automatically prioritizes likely candidates based on semantic relevance, reducing computational time while maintaining high accuracy. This selective computation approach balances precision requirements with time efficiency.
Solution Approach 2:
The system incorporates feedback mechanisms where the attention weights and operation selections are refined based on the query context and intermediate results. The probabilistic computation provides feedback signals that guide the search process, allowing the system to converge on accurate answers more efficiently. This feedback loop enables the system to achieve high measurement precision while minimizing unnecessary computational steps.
Data Source
AI summary
Systems and methods for natural language processing are described. One or more embodiments of the present disclosure receive a query related to information in a table, compute an operation selector by combining the query with an operation embedding representing a plurality of table operations, compute a column selector by combining the query with a weighted operation embedding, compute a row selector based on the operation selector and the column selector, compute a probability value for a cell in the table based on the row selector and the column selector, where the probability value represents a probability that the cell provides an answer to the query, and transmit contents of the cell based on the probability value.