Differentially Private Parse Tree Synthesis for Privacy-Preserving NLP Training
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Solution Overview
Problem
Traditional task-oriented natural language dialogue models have limited linguistic and functional coverage due to privacy controls associated with user data, leading to the need for improved training methods that preserve user privacy while enhancing model capabilities.
Innovation Solution
A differentially private approach is employed to synthesize a training dataset by modeling parse trees and conditional distributions of utterances, using noise-calibrated gradients to ensure privacy while generating synthesized utterances that mimic real user interactions, thus improving model training without compromising user privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional training methods using real user data are employed, then model training quality is improved, but user privacy is compromised
Solution Approach 1:
The patent creates synthesized training data that copies the structural and statistical properties of real user interactions without using actual user data. Parse trees from real interactions are used as templates to generate synthetic utterances that mimic real conversation patterns while preserving no identifiable user information, thus maintaining training quality without compromising privacy
Solution Approach 2:
The patent introduces parse trees as an intermediary representation layer between real user utterances and the training dataset. Real user interactions are converted into abstract parse tree structures, which then serve as templates for generating synthetic utterances. This intermediary step eliminates direct exposure of user data while preserving the linguistic patterns needed for effective model training
2Object-affected harmful factors
If privacy controls are implemented to protect user data, then user privacy is preserved, but linguistic and functional coverage of the model is limited
Solution Approach 1:
The synthesized training data copies the diverse linguistic patterns and functional structures from real user interactions at the parse tree level. By generating multiple synthetic utterances from each parse tree template and varying surface forms while maintaining structural integrity, the method achieves comprehensive linguistic and functional coverage without using actual user data
Solution Approach 2:
The patent applies parameter changes by systematically varying surface-level utterance parameters (word choices, phrasing, syntax variations) while keeping the underlying parse tree structures constant. This generates diverse synthetic utterances that expand linguistic coverage while the controlled parameter changes ensure privacy preservation through differential privacy mechanisms
3Productivity
If synthesized training data is generated without parse tree modeling, then data generation speed is improved, but utterance fluency and diversity are reduced
Solution Approach 1:
The patent performs preliminary action by pre-processing real user interactions into parse tree structures before synthesis. These pre-computed parse trees serve as ready-made templates that can be efficiently reused to generate multiple synthetic utterances, maintaining both generation speed and linguistic quality through the reusable template mechanism
Solution Approach 2:
The patent segments the data generation process into distinct stages: (1) parsing real interactions into structured trees, (2) sampling from parsed structures, and (3) generating surface utterances from templates. This segmentation allows efficient parallel processing while maintaining fluency through structured templates, achieving both speed and quality
Data Source
AI summary
Examples are disclosed that related to synthesizing a dataset of utterances in an automated manner using a computer while preserving user privacy. The synthesized dataset of utterances is usable to train a machine learning model. In one example, a differentially private parse tree generation model is trained based at least on private parse trees of a private utterance-parse tree dataset. A differentially private parse-to-utterance model is trained based at least on private utterances and corresponding private parse trees of the private utterance-parse tree dataset. A synthesized parse tree dataset is generated. The synthesized parse tree dataset includes synthesized parse trees sampled at random from the trained differentially private parse tree generation model. A synthesized utterance dataset is generated, via the trained differentially private parse-to-utterance model. The synthesized utterance dataset includes synthesized utterances that are generated based at least on the synthesized parse trees of the synthesized parse tree dataset.