Tabular Question Answering Through Text Mapping and Scalar Queries
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Solution Overview
Problem
Modern natural language processing (NLP) systems struggle to effectively retrieve information from tabular data due to a lack of data relationship understanding and proper grammatical representation, failing to process structured tabular data with semantic and grammatical details.
Innovation Solution
An intelligent table-based question answering system using artificial intelligence and NLP techniques processes tabular data by converting structured data into grammatically accurate forms, mapping entity relations, and leveraging NLP for natural-language responses, capable of handling both textual and scalar queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional NLP systems process tabular data, then they can handle unstructured text data, but they fail to understand data relationships and grammatical structures in structured tabular data
Solution Approach 1:
The patent introduces an intermediary module that converts structured tabular data into unstructured text format with grammatical structures. This intermediary transformation layer enables traditional NLP systems to process tabular data by translating it into a format they can handle, while preserving data relationships through grammatical constructions. The converter acts as a bridge between the structured data domain and the unstructured text processing domain.
Solution Approach 2:
The system changes the parameter representation of tabular data by transforming structured data parameters (rows, columns, cells) into linguistic parameters (subjects, predicates, objects, modifiers). This parameter transformation allows the data to be processed through NLP pipelines while maintaining the semantic relationships inherent in the original tabular structure.
2Ease of operation
If the system converts structured tabular data into natural language text, then it enables NLP processing, but it may lose the precise structural relationships of the original data
Solution Approach 1:
The system performs preliminary action by pre-processing the tabular data to identify and mark data relationships before conversion to text. Relationship markers and structural indicators are embedded during the conversion process, allowing the NLP system to later reconstruct the original tabular relationships from the natural language text without losing critical structural information.
Solution Approach 2:
The system implements feedback mechanisms where the converted text is analyzed to verify that data relationships are preserved. The system iteratively refines the conversion process by comparing the structural relationships in the original table with those inferred from the generated text, ensuring that no critical information is lost during transformation.
3Adaptability or versatility
If the system processes both textual queries and scalar queries requiring mathematical operations, then it increases versatility, but it increases system complexity
Solution Approach 1:
The patent implements a universal query processing architecture that can handle both textual queries and scalar queries requiring mathematical operations through a single integrated system. The system uses a unified representation framework that accommodates different query types, allowing the same NLP processing pipeline to interpret various query formats and route them to appropriate processing functions without requiring separate specialized systems.
Data Source
AI summary
An intelligent table-based question answering system receives an input user query requesting information from an input table to generate a response to the input query. Two types of user queries including textual user queries and scalar user queries that require at least one mathematical operation to be executed can be processed for answer generation. The input table is processed to generate a paragraph of table text which includes row and column information extracted from the input table. The input query is provided along with the paragraph of table text to a deep Quans model which outputs a candidate answer that forms a textual portion of the response. Also, the candidate answer is reverse mapped to the input table and a portion of the input table including the candidate answer is provided as a tabular portion of the response.