Table-to-Text Generation with Logic-Type Control
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Solution Overview
Problem
Current table-to-text generation methods either lack control over insight type or over-specify, resulting in reduced user interest and coverage, as they fail to generate diverse and high-quality natural language statements specific to user preferences or logic inference types.
Innovation Solution
A table-to-text generation model is trained using logic-types as input, enabling it to generate natural language statements specific to user-specified types and, in the absence of specification, produce multiple statements across different logic-types, enhancing diversity and controllability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current table-to-text generation methods are used, then generation speed is maintained, but control over insight type is lost and diversity is reduced
Solution Approach 1:
The model is segmented into multiple specialized sub-models, each trained to generate statements of a specific logic-type (e.g., aggregation, comparison, classification). When a user requests statements, the system routes the request to the appropriate sub-model based on the desired logic-type, enabling precise control over insight type without requiring one monolithic complex model to handle all types.
Solution Approach 2:
The system implements a universal framework that can handle multiple logic-types through a common architecture. The model family shares underlying components and training methodologies while specializing in different logic-types, allowing the system to provide versatile control across multiple insight types without proportionally increasing overall complexity.
2Adaptability or versatility
If current table-to-text generation methods are used, then processing time is reduced, but semantic diversity is limited
Solution Approach 1:
Multiple specialized models are pre-trained in advance on different logic-types during the model development phase. When generating statements, the system can immediately query the appropriate pre-trained model without performing time-consuming training or adaptation, thus achieving high semantic diversity while maintaining fast generation times.
3Manufacturing precision
If user-specified logic-type control is implemented, then statement quality for specific types improves, but coverage of multiple types decreases
Solution Approach 1:
The system dynamically adapts its behavior based on user input. When a user specifies a particular logic-type, the system activates the corresponding specialized model to ensure high statement quality for that type. When no logic-type is specified, the system can switch to a mode that queries multiple models or uses a default model to provide broader coverage across different logic-types, thus balancing quality and versatility dynamically.
Data Source
AI summary
A table-to-text (T2T) generation model provides type control and semantic diversity. A method, system, and computer program product are configured to: train a model to generate one or more logic-type-specific natural language statements based on tabular data; in response to receiving a first input comprising first input data with a user-specified logic-type, the trained model generating a first logic-type-specific natural language statement based on the first input data and the user-specified logic-type; and in response to receiving a second input comprising second input data without a user-specified logic-type, the trained model generating plural second logic-type-specific natural language statements based on the second input data, wherein respective ones of the second logic-type-specific natural language statements are generated according to respective ones of plural predefined logic-types.