NLP System Automating Annotation via Model Prediction
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Solution Overview
Problem
Current speech-processing systems require human annotation to recognize new functions and invocations, which is inefficient and relies heavily on human interaction.
Innovation Solution
A natural-language processing system that uses a trained model to determine annotation data and predict the effect of re-training components, reducing the need for human annotation by processing input data to encode text and compare it with similar training data to recognize new functions and invocations automatically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human annotation is used to recognize new functions and invocations, then the accuracy of speech recognition is improved, but the productivity and efficiency of the system deteriorates due to heavy reliance on human interaction
Solution Approach 1:
The system uses automated machine learning models to perform annotation of new functions and invocations without human intervention. The model processes input data, encodes text, compares it with training data, and automatically recognizes new functions, enabling the system to serve itself rather than relying on human annotators.
Solution Approach 2:
The patent replaces the mechanical process of human annotation with an automated computational process. The system uses trained models, text encoding, and data comparison algorithms to substitute human interaction with machine-based function recognition, thereby improving efficiency while maintaining accuracy.
2Reliability
If human annotation is required for every new function, then the reliability of annotation data is improved, but the device complexity and operational difficulty increase
Solution Approach 1:
The system automatically processes and annotates new functions using machine learning models without requiring human operators. The automated process handles text encoding, data comparison, and function recognition, reducing operational complexity while maintaining reliable annotation through the model's learning capabilities.
Solution Approach 2:
The system uses pre-trained models that have been prepared in advance to handle new functions. The model is trained on existing data beforehand, so when new functions are encountered, the pre-trained model can process them automatically without requiring complex manual annotation procedures.
3Adaptability or versatility
If more training data is collected and processed, then the adaptability of the system to new functions is improved, but the loss of time and computational resources increases
Solution Approach 1:
The system performs preliminary training of the machine learning model on training data in advance. Once trained, the model can quickly process and recognize new functions without requiring extensive processing time for each new input, thus reducing the time loss associated with processing training data while maintaining high adaptability.
Solution Approach 2:
The system uses text encoding to create compressed representations (copies) of training data. By encoding text and comparing encoded forms rather than processing raw text data, the system reduces computational time and resources while maintaining the ability to recognize patterns and adapt to new functions.
Data Source
AI summary
Example embodiments provide techniques for configuring a natural-language processing system to perform a new function given at least one sample invocation of the function. The training data consisting of the sample invocation may be augmented by determining which subset of available training data most closely resembles the sample invocation and/or function. The effect of re-training a component this this augmented training data may be determined, and an annotator may review any annotations corresponding to the invocation if the effect is large.