Grammar-Based Labeling for Dialog System Training Data
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Solution Overview
Problem
Current methods for training statistical models in dialog systems require large amounts of manually labeled data, which is time-consuming, labor-intensive, and costly.
Innovation Solution
A grammar-based labeling scheme is used to generate labeled sentences, where a grammar is constructed manually or adapted based on application domain requirements, and an annotation schema is created to include syntactic and semantic information, allowing for automated generation of labeled sentences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling methods are used to create training data, then labeling accuracy and data quality are improved, but time consumption and labor costs increase significantly
Solution Approach 1:
The patent introduces an annotation schema as an intermediary framework that structures the labeling process. This schema serves as a mediator between raw text data and labeled training data, providing a systematic approach that reduces manual effort while maintaining consistency and accuracy in labeling across large datasets
Solution Approach 2:
The patent segments the labeling process into structured components through the annotation schema, which divides complex labeling tasks into manageable elements. This segmentation allows for more efficient processing and reduces the time required while preserving labeling quality through systematic organization
2Measurement precision
If manual labeling methods are used to create training data, then data quality is improved, but cost and effort increase significantly
Solution Approach 1:
The annotation schema acts as a cost-effective intermediary that standardizes the labeling process. By providing a pre-defined structure for annotations, it reduces the effort and cost associated with manual labeling while maintaining data quality through consistent application of labeling standards across the dataset
Solution Approach 2:
The patent changes the parameters of the labeling process by introducing structured annotation schemas that define specific labeling parameters and constraints. This transformation converts an unstructured, high-cost manual process into a more systematic approach that reduces effort and cost while preserving data quality
3Reliability
If large amounts of labeled training data are obtained through manual methods, then model training quality is improved, but productivity decreases
Solution Approach 1:
The annotation schema serves as a productivity-enhancing intermediary that enables faster data generation. By providing a structured framework for labeling, it allows for more rapid processing of text data into training examples while maintaining the quality needed for reliable model training
Solution Approach 2:
The patent applies preliminary action by pre-defining annotation schemas and labeling structures before the actual labeling process. This preparation work is done once and then reused across large datasets, significantly improving productivity while ensuring consistent data quality for model training
Data Source
AI summary
Embodiments of a dialog system that utilizes grammar-based labeling scheme to generate labeled sentences for use in training statistical models. During the process of training data development, a grammar is constructed manually based on the application domain or adapted from a general grammar rule. An annotation schema is created accordingly based on the application requirements, such as syntactic and semantic information. Such information is then included in the grammar specification. After the labeled grammar is constructed, a generation algorithm is then used to generate sentences for training various statistical models.