Data-to-text generation with semantic fidelity classifier
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Solution Overview
Problem
Current data-to-text generation systems face challenges in generalizing to unseen domains and maintaining semantic fidelity, with pipeline-based approaches being complex and prone to errors, and end-to-end methods struggling with hallucination, omission, repetition, and value errors.
Innovation Solution
The implementation of a domain-independent data-to-text system using a pre-trained language model with fine-grained state embeddings and a weakly-supervised semantic fidelity classifier to detect and correct errors, ensuring accurate text generation across domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pipeline-based approaches are used for data-to-text generation, then the system can process complex tasks through multiple specialized components, but the device complexity increases and the system becomes prone to errors between components
Solution Approach 1:
The patent merges multiple specialized pipeline components (content selection, text structuring, sentence aggregation) into a single end-to-end deep learning model. This integration eliminates the interfaces between components where errors propagate, reducing overall system complexity while maintaining the functional capabilities of each sub-task through unified model architecture.
Solution Approach 2:
The end-to-end model performs multiple functions (content selection, text structuring, sentence aggregation) within a single universal framework. This multi-functional approach allows the system to handle diverse data-to-text generation tasks without requiring separate specialized components for each function, thereby reducing device complexity.
2Device complexity
If end-to-end methods are used for data-to-text generation, then the device complexity is reduced, but the system suffers from hallucination, omission, repetition, and value errors
Solution Approach 1:
The system performs preliminary training on large-scale domain-specific corpora to pre-learn linguistic patterns, domain terminology, and factual relationships before actual text generation. This preliminary action equips the model with domain knowledge that reduces hallucination and improves semantic fidelity during deployment.
Solution Approach 2:
The system implements feedback mechanisms during training and inference, including attention mechanisms that monitor generated content against input data, and post-generation verification steps that detect and correct hallucinations, omissions, and repetitions. This feedback loop continuously improves semantic fidelity.
3Manufacturing precision
If domain-specific training data is used for each sub-task in pipeline-based approaches, then the manufacturing precision of each component is improved, but the loss of time for data preparation and model training increases
Solution Approach 1:
The patent combines multiple domain-specific training tasks into a single unified training process. Instead of separately training models for content selection, text structuring, and sentence aggregation with different domain corpora, the end-to-end model learns all these functions simultaneously from integrated domain data, significantly reducing data preparation and training time.
Solution Approach 2:
The unified model serves multiple functions across different domains through universal training on diverse domain-specific corpora. This multi-functional training approach allows the system to achieve high accuracy in various domains without requiring separate training processes for each, thereby reducing time loss.
4Adaptability or versatility
If traditional data-to-text systems are used, then they can generate text from structured data, but they fail to generalize to unseen domains and maintain semantic fidelity
Solution Approach 1:
The system performs preliminary training on large-scale, diverse domain-specific corpora before deployment. This pre-training establishes a robust foundation of domain knowledge and linguistic patterns that enables the model to generalize to unseen domains while maintaining semantic accuracy through transfer learning.
Solution Approach 2:
The system dynamically adjusts model parameters and attention weights based on the specific domain characteristics of the input data. This parameter adaptation allows the model to optimize its behavior for different domains while maintaining core semantic fidelity, achieving both adaptability and reliability.
Data Source
AI summary
Devices and techniques are generally described for data-to-text generation. In various examples, a first machine learned model may receive first data including a structured representation of linguistic data. In various examples, the first machine learned model may generate first output data comprising a first natural language representation of the first data. In at least some examples, a second machine learning model may determine second data indicating that the first natural language representation is a semantically accurate representation of the first data. In some examples, the first output data may be selected for output based at least in part on the second data.