Dialog System Training via Database-Derived Data
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Solution Overview
Problem
Conventional dialog systems require lengthy and computationally intensive training periods for machine learning models, with traditional data labeling being tedious and time-consuming, limiting the practicality of using large datasets for improved accuracy.
Innovation Solution
The use of preexisting databases to derive training data, allowing for rapid training and improved accuracy of machine learning models by leveraging database columns to generate training data, reducing or eliminating the need for manual labeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large datasets are used to train machine learning models, then model accuracy is improved, but the time and resources required for training increase
Solution Approach 1:
The patent applies preliminary action by pre-extracting and storing training data in database tables before the actual machine learning training process. Database tables are populated with training examples, entity types, and contextual information in advance, so that when training is needed, the system can directly query and use this pre-prepared data without performing time-consuming extraction and labeling operations during the training phase itself.
Solution Approach 2:
The patent uses copying by creating structured representations of training data in database tables that mirror the format needed for machine learning training. Instead of working directly with raw unstructured data during training, the system copies and structures the essential training information (entities, types, contexts) into database tables with schemas that facilitate efficient retrieval and processing during the training process.
2Manufacturing precision
If manual data labeling is performed to create training data, then data quality is improved, but the process becomes tedious and time-consuming
Solution Approach 1:
The patent applies self-service by enabling the system to automatically generate and structure training data without requiring manual human labeling. The database tables are populated programmatically by extracting information from existing data sources, applying predefined schemas and entity type classifications, and organizing the data in training-appropriate formats automatically, eliminating the need for tedious manual labeling while maintaining data quality through structured extraction processes.
3Reliability
If traditional training methods are used, then model performance is improved, but computational resources and training duration increase
Solution Approach 1:
The patent applies extraction by separating the data preparation function from the training function. The system extracts only the essential training elements (entities, types, contextual relationships) into database tables, leaving out unnecessary raw data and processing steps. This extraction approach allows the training process to work with a focused, pre-processed subset of data that contains only what is needed for training, reducing computational overhead while maintaining model performance.
Data Source
AI summary
Techniques are described for training and executing a machine learning model using data derived from a database. A dialog system uses data from the database to generate related training data for natural language understanding applications. The generated training data is then used to train a machine learning model. This enables the dialog system to leverage a large amount of available data to speed up the training process as compared to conventional labeling techniques. The dialog system uses the trained machine learning model to identify a named entity from a received spoken utterance and generate and output a speech response based upon the identified named entity.