Semantic Clustering for Self-Learning NLP Failure Remediation
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Solution Overview
Problem
Natural language processing systems face challenges in identifying the cause of failures in multi-stage processing, leading to sub-optimal user experiences, which are difficult to diagnose manually due to the complexity and large volume of inputs.
Innovation Solution
A self-learning system that automatically detects and remediates processing defects in multi-stage systems by using machine learning techniques, including embedding, clustering, and decision components to identify and recommend fixes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If manual diagnosis methods are used to identify processing defects, then system complexity can be managed, but productivity and efficiency deteriorate due to large volume of inputs and time-consuming analysis
Solution Approach 1:
The system performs self-diagnosis by automatically analyzing processing defects without requiring manual intervention. The defect identification system autonomously processes inputs, identifies issues, and generates remediation recommendations, enabling the system to serve itself in detecting and addressing its own problems.
Solution Approach 2:
Manual diagnosis methods are replaced with automated machine learning-based analysis. The system uses computational algorithms to process and analyze data, substituting human manual inspection with automated electronic processing that handles large volumes of inputs efficiently.
2Productivity
If automated defect identification systems are implemented, then productivity improves, but device complexity increases due to multiple processing stages and components
Solution Approach 1:
The defect identification system is divided into distinct functional components: embedding generation, clustering analysis, defect identification, and remediation recommendation. Each component handles a specific aspect of the analysis, making the overall complex system manageable through modular organization.
Solution Approach 2:
Embeddings serve as an intermediary representation that transforms raw inputs into a standardized format suitable for clustering and analysis. This intermediate layer simplifies the processing of complex inputs by converting them into comparable vector representations.
3Measurement precision
If manual investigation is performed to determine cause of failure, then measurement precision can be maintained, but loss of time increases due to large number of utterances and processing stages
Solution Approach 1:
The system performs preliminary analysis by generating embeddings and clustering inputs before detailed defect identification. This preliminary organization of data into groups enables faster subsequent analysis by reducing the search space and focusing computational resources on relevant clusters.
Solution Approach 2:
The system creates simplified representations (embeddings) that copy the essential characteristics of complex inputs. These embeddings preserve the semantic meaning and relationships of original utterances while enabling efficient computational processing and comparison.
Data Source
AI summary
Devices and techniques are generally described for semantic clustering and fixing of like-failed processing inputs. In some examples, first embedding data representing a first input may be generated. A first cluster of embeddings including the first embedding data may be generated. Second embedding data of the first cluster may be determined. First metadata for the first input may be determined. The first metadata may be associated with processing of the first input by the processing system. Second metadata for the second input may be determined. A first remedial action may be determined for processing the first input based at least in part on the first metadata and the second metadata.