Neural Language Model for Automated Data Annotation
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Solution Overview
Problem
Conventional methods for generating training data for machine learning models are highly dependent on human expertise, especially for domain-specific data, making the process complex and unreliable.
Innovation Solution
A method and system for training a neural language-based model that receives data from multiple sources, generates a category vocabulary, identifies tokens indicative of pre-defined data classes, masks and predicts these tokens using contextual vectors, and implements a self-training process to annotate data with minimal human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human expertise is used to label and generate training data, then the quality and accuracy of data annotation is improved, but the process becomes highly dependent on human agency and is time-consuming
Solution Approach 1:
The system performs preliminary actions by pre-processing the dataset to identify category indicator tokens before the main annotation task. This includes generating category vocabularies, identifying tokens indicative of pre-defined data classes, and creating training data templates in advance, which streamlines the subsequent annotation process and reduces overall time requirements
Solution Approach 2:
The system enables self-service by automatically generating training data through machine learning models without requiring continuous human intervention. The model learns from initial labeled data and autonomously annotates remaining data, reducing dependency on human agency while maintaining annotation quality through iterative self-training processes
2Reliability
If human expertise is used for domain-specific data labeling, then the reliability of data class identification is improved, but the complexity of the process increases due to required expertise
Solution Approach 1:
The system introduces an intermediary machine learning model that acts as a bridge between raw domain-specific data and final annotations. This model learns domain-specific patterns from initial training data and serves as an intermediary classifier, reducing the need for direct human expertise in the annotation process while maintaining reliability through learned domain knowledge
Solution Approach 2:
The system changes parameters by transforming the annotation process from rule-based human judgment to model-based probabilistic classification. This involves converting qualitative domain expertise into quantitative model parameters through training, allowing the system to handle domain-specific data reliably without requiring humans to directly apply complex domain knowledge
3Quantity of substance
If more training data is generated manually, then the availability of training data is improved, but the productivity of the overall system decreases due to manual effort requirements
Solution Approach 1:
The system applies partial action by manually labeling only a small portion of the dataset (e.g., 10-20%) to create initial training data, while the remaining majority is annotated automatically by the model. This excessive manual action on a small scale enables the model to learn and perform the bulk of annotation work, significantly increasing productivity while still generating sufficient training data quantity
4Ease of operation
If conventional tools are used for data annotation, then the ease of operation is maintained, but the adaptability to domain-specific data classes deteriorates
Solution Approach 1:
The system introduces dynamics by making the annotation process adaptive rather than static. The machine learning model learns from domain-specific data patterns and continuously improves its performance on domain-specific data classes. The system dynamically adjusts to different domains by retraining on domain-specific training data, maintaining ease of operation through automated processes while gaining adaptability to specific domains
Data Source
AI summary
A method and a system for training a neural language-based model for data annotation are disclosed. The method includes: receiving, via a communication interface, a set of data from a plurality of sources, each item of the set of data being associated with a pre-defined data class; generating at least one category vocabulary for the pre-defined data class; identifying at least one token in the set of data based on an analysis of the set of data, wherein the at least one token corresponds to a category indicator of the pre-defined data class; masking the at least one token; feeding the at least one masked token together with a corresponding contextual vector to the neural language-based model; and predicting, using the neural language-based model, a class of the at least one masked token using the corresponding contextual vector.