Sentence Weight Assessment for NLP Explainability
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Solution Overview
Problem
Conventional AI applications lack explainability, making it difficult to understand the reasoning behind predictions, especially in applications with 'big data' where misinterpretation can be costly.
Innovation Solution
A computer-implemented method and system that assesses predictive weights of sentences in a document by generating variants of the document, making predictions using a trained model, and determining sentence weights based on confidence scores, with visual effects presented to enhance readability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional AI applications use machine learning algorithms to generate predictions, then prediction accuracy is improved, but explainability of the predictions deteriorates
Solution Approach 1:
The patent segments the document into individual sentences and evaluates each sentence's contribution to the prediction separately. By generating variants that exclude specific sentences and comparing confidence scores, the system identifies which sentence segments most influence the prediction, thereby providing explainability without sacrificing the overall prediction accuracy of the machine learning model.
Solution Approach 2:
The patent introduces an intermediary evaluation process that acts as a bridge between the black-box prediction model and the user. This intermediary system generates document variants, computes confidence score differences, and translates the model's internal reasoning into interpretable sentence-level contributions, allowing users to understand predictions without modifying the underlying accurate prediction model.
2Productivity
If AI applications process big data without explanations, then processing speed is improved, but misinterpretation costs increase
Solution Approach 1:
The patent applies partial action by selectively analyzing only the most influential sentences rather than examining every piece of big data in detail. By identifying and highlighting key sentences that drive predictions, the system maintains fast processing speeds while providing sufficient explanation to prevent costly misinterpretations, avoiding the need to thoroughly analyze all data points.
Solution Approach 2:
The patent applies local quality by providing explanations at the sentence level rather than requiring comprehensive explanations for all data. This allows the system to maintain high processing speed for big data while ensuring interpretation accuracy for critical local elements (key sentences) that most affect the prediction outcome.
3Loss of information
If AI applications provide detailed explanations for predictions, then explainability is improved, but computational complexity increases
Solution Approach 1:
The patent extracts only the essential information needed for explanation by identifying specific sentences that contribute most to the prediction. Rather than analyzing or processing the entire document for explanation purposes, the system extracts key sentence-level contributions through confidence score comparisons, reducing computational complexity while maintaining explainability.
Solution Approach 2:
The patent uses partial action by focusing computational resources on evaluating only the marginal contribution of sentences through confidence score differences, rather than performing exhaustive analysis of all document elements. This approach provides sufficient explainability with reduced computational complexity by acting on a subset of the full information space.
Data Source
AI summary
The present disclosure relates to a method for evaluating predictive weights of individual sentences in a document and visually representing the sentences based on the weights. The document is from a document repository and variants of the document are generated by excluding certain number of sentences from the document. By use of a trained prediction model that provides a confidence score for each prediction, the document and the variants are predicted, and respective confidence scores are determined. A weight of a sentence for all sentences in the document is determined by use of the confidence scores respective to the predictions based on the document and each of the variants. The sentences in the document are presented in a manner visually differentiated by respective weights of the sentences in the document.