Sentence Pair Ranking for Virtual Assistant NLP
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Solution Overview
Problem
Virtual assistants face challenges in accurately ranking documents for user queries due to domain-specific text handling, leading to 'catastrophic forgetting' during fine-tuning of pre-trained models, resulting in loss of knowledge and failure in tasks where the model previously performed well.
Innovation Solution
A computer-implemented method for training a machine learning model through sentence pair matching in natural language processing, where sentence pairs are prepared from a training dataset, ranked based on similarity, and outmatched pairs are identified to tune the model parameters, enhancing the model's responsiveness to user queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If pre-trained models are fine-tuned on domain-specific text, then the model adapts to the target task and domain, but the model loses previously learned knowledge due to catastrophic forgetting
Solution Approach 1:
The patent applies preliminary action by performing entropy calculation and identifying uncertain predictions before the fine-tuning process. The method calculates entropy of predictions on a validation set, identifies sentences with high entropy (uncertain predictions), and uses only these uncertain sentences for fine-tuning. This preliminary identification ensures that the model adapts to domain-specific text while preserving previously learned knowledge by avoiding retraining on well-performing examples.
Solution Approach 2:
The patent applies local quality by selectively applying fine-tuning only to specific portions of the training data (sentences with high entropy/uncertain predictions) rather than uniformly processing all data. The method identifies local regions of uncertainty in the model's prediction space and concentrates adaptation efforts there, while leaving well-performing regions unchanged, thus preserving general knowledge while adapting to domain specifics.
2Measurement precision
If the model is fine-tuned on target task data, then the model improves performance on the specific task, but the model starts failing tasks where it previously performed well
Solution Approach 1:
The patent applies feedback by using entropy calculation as a feedback mechanism to monitor model uncertainty during the fine-tuning process. The entropy of predictions on the validation set serves as feedback to identify which sentences require adaptation. This feedback loop ensures that the model improves on uncertain predictions while maintaining stability on well-performing tasks, as evidenced by consistent performance metrics across different evaluation sets.
3Adaptability or versatility
If traditional fine-tuning methods are used, then the model learns domain-specific patterns, but the process is complex and requires careful parameter adjustment
Solution Approach 1:
The patent applies self-service by enabling the model to automatically identify its own learning needs through entropy calculation. The system autonomously determines which sentences require fine-tuning based on its own prediction uncertainty, eliminating the need for complex external parameter adjustment and manual intervention. The method automatically selects training samples and adjusts the fine-tuning process based on the model's own performance characteristics.
Data Source
AI summary
The present disclosure relates to computer-implemented methods, systems, and/or computer program products for training a machine learning model for sentence pair matching in natural language processing. For example, computer-implemented methods described herein can include preparing sentence pairs from a training dataset, where each sentence pair comprises a pairing of a search string and a target document from the training dataset. The computer-implemented method can also include ranking the sentence pairs based on an amount of similarity between the search string and the target document. Further, the computer-implemented method can include identifying an outmatched sentence pair. The target document of the outmatched sentence pair is a non-responsive document to the search string. The computer-implemented method can moreover include utilizing the outmatched sentence pair to tune a parameter of a natural language processing model to generate a trained model.