Synthetic NLP Payload Generation for Performance Testing
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Solution Overview
Problem
Natural language processing (NLP) systems face challenges in accurately gauging performance due to variations in text patterns, especially when supporting multiple languages, and require representative training data that mirrors production text, while direct testing on user data violates data protection policies.
Innovation Solution
A method for generating NLP text payloads by extracting characterizing information from input text, such as part-of-speech patterns and term frequencies, to create synthetic text that mirrors production text patterns, allowing for performance testing without storing raw user data, thus ensuring compliance with data protection regulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If representative training data is used to accurately gauge NLP system performance, then measurement precision is improved, but data protection policies are violated due to direct testing on user data
Solution Approach 1:
The patent creates synthetic test data that copies the essential patterns and characteristics of production text data without using actual user data. This involves generating artificial text samples that mirror the statistical properties, language patterns, and structural features of real data, enabling accurate performance testing while maintaining data protection compliance
Solution Approach 2:
The patent introduces an intermediary layer between the NLP system and actual user data by using synthetic proxy data. This intermediary dataset serves as a safe substitute that preserves the informational content needed for performance evaluation while eliminating the harmful aspect of exposing sensitive user information
2Adaptability or versatility
If NLP systems support multiple languages and dialects, then adaptability is improved, but measurement precision deteriorates due to difficulty in obtaining representative training data
Solution Approach 1:
The patent creates a universal synthetic data generation framework that can produce representative test data for multiple languages and dialects using a single system. The approach extracts language-agnostic patterns from production data and applies them across different language contexts, enabling consistent performance measurement across diverse linguistic scenarios
Solution Approach 2:
The patent adjusts parameters of synthetic data generation based on the specific language or dialect being tested. By modifying statistical properties, vocabulary distributions, and structural patterns in the synthetic data to match target languages, the system maintains measurement precision across multiple languages without requiring separate data collection processes
Data Source
AI summary
An input text that is also transmitted to a text processing service (e.g., a cloud based text processing service) is received. Characterizing information (e.g., contiguous parts of speech, terms used per part of speech, payload length, etc.) is extracted from the input text. A text payload is generated using the characterizing information. A performance test is run on the text payload. The performance test can include performing at least one selected from a group consisting of: sentiment analysis on the text payload, entity analysis on the text payload, content classification on the text payload, and syntax analysis on the text payload. The performance test can yield a processing time required to perform the performance test. Memory and processing power resource allocation to the text processing service can be altered based on the processing time of the performance test.