Task-Adapted Generative Model for Synthetic NLU Training Data
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Solution Overview
Problem
Existing machine learning models for natural language understanding lack effectiveness in generating task-specific synthetic training examples, particularly in dialog-based systems, due to insufficient human-labeled data and general-purpose training data that fails to produce relevant and useful synthetic utterances.
Innovation Solution
A method involving a three-stage training process for generative models, starting with pretraining on a general-purpose corpus, followed by semantically conditioning on dialog act-labeled data, and tuning with task-specific seed examples to adapt the model for generating high-quality synthetic training examples suitable for specific tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If general-purpose training data is used to train generative models, then the models can be trained with abundant data, but the generated synthetic utterances lack task-specific relevance and effectiveness
Solution Approach 1:
The patent segments the training process into three distinct stages: (1) pretraining on general-purpose corpus to learn basic language patterns, (2) semantically conditioning on dialog act-labeled data to acquire task awareness, and (3) tuning with task-specific seed examples to achieve task expertise. This segmentation allows the model to progressively specialize while maintaining language generation capabilities.
Solution Approach 2:
The patent applies preliminary action by first pretraining the model on general-purpose data to establish foundational language understanding, then sequentially adding task-specific knowledge through semantically conditioned pretraining and fine-tuning. This preliminary preparation enables the model to effectively generate task-relevant synthetic data when needed.
2Manufacturing precision
If human-labeled task-specific data is collected to improve model effectiveness, then the quality and relevance of training data increase, but the time and resources required for data collection and labeling increase significantly
Solution Approach 1:
The patent implements self-service by enabling the generative model to automatically generate high-quality task-specific training examples without requiring extensive human labeling. The model uses a small set of seed examples to learn task patterns and then autonomously generates additional training data, significantly reducing human intervention and time investment.
Solution Approach 2:
The patent changes the parameter of training data quantity by generating synthetic examples that augment the limited seed data. This parameter change allows the model to achieve better performance without proportionally increasing the time and resources for data collection, as the synthetic data is generated automatically rather than manually labeled.
3Manufacturing precision
If the generative model is highly specialized for a specific task, then the quality of synthetic utterances improves, but the model loses versatility to handle other tasks
Solution Approach 1:
The patent achieves universality by designing a multi-stage training framework where the model maintains general language generation capabilities from the pretraining stage while acquiring task-specific expertise through subsequent semantically conditioned pretraining and fine-tuning. This layered approach allows the model to be adapted to different tasks by changing the conditioning inputs and seed examples while keeping the core architecture intact.
Data Source
AI summary
This document relates to machine learning. One example includes a method or technique that can be performed on a computing device. The method or technique can include obtaining a task-semantically-conditioned generative model that has been pretrained based at least on a first training data set having unlabeled training examples and semantically conditioned based at least on a second training data set having dialog act-labeled utterances. The method or technique can also include inputting dialog acts into the semantically-conditioned generative model and obtaining synthetic utterances that are output by the semantically-conditioned generative model. The method or technique can also include outputting the synthetic utterances.