Feedback-Driven Sentiment Model Finetuning for New Domains
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Solution Overview
Problem
Existing sentiment analysis models produce incorrect predictions when encountering new domains with unfamiliar vocabulary or words that have specific meanings, leading to deployment challenges.
Innovation Solution
Implement an interactive process allowing users to provide feedback on model predictions, which is used to retrain or finetune the model for improved performance in relevant domains, and adjust prediction thresholds based on user feedback for optimal classification precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a pretrained sentiment analysis model is deployed, then automated classification can be performed, but the model produces incorrect predictions when encountering new domains with unfamiliar vocabulary
Solution Approach 1:
The system implements a feedback mechanism where users can correct model predictions by providing annotated feedback on utterances. This feedback is then used to finetune the model, allowing it to learn from its mistakes and improve its performance on new domains. The feedback loop enables the model to adapt to domain-specific vocabulary and nuances that it initially misinterpreted.
Solution Approach 2:
The model parameters are dynamically adjusted through finetuning based on user feedback. The system modifies the internal parameters of the pretrained model by training it on additional annotated data from the specific domain, thereby changing its decision boundaries and improving its accuracy for that domain without requiring complete retraining.
2Reliability
If the model is finetuned on domain-specific data, then prediction accuracy improves, but the complexity of the training process increases
Solution Approach 1:
The system performs preliminary actions by first collecting and annotating a dataset of utterances with correct sentiment labels before finetuning the model. This pre-prepared annotated data serves as the training material, simplifying the subsequent finetuning process. The preliminary data collection and annotation phase is completed before the actual model training begins.
Solution Approach 2:
The system uses the pretrained model as a foundation and creates a copy of it for finetuning. Rather than training a model from scratch, the system copies the pretrained model's architecture and parameters, then adapts it to the specific domain using domain-specific annotated data. This copying approach reduces the complexity compared to training from scratch while maintaining the benefits of domain-specific customization.
3Adaptability or versatility
If user feedback is collected for model improvement, then the model adapts to domain-specific terminology, but the time required for feedback collection and processing increases
Solution Approach 1:
The system implements continuous finetuning where the model is continuously updated with new domain-specific feedback data. Rather than requiring a one-time lengthy training process, the system continuously integrates feedback in incremental steps, allowing the model to adapt progressively. This continuous action distributes the time requirement over multiple smaller training cycles.
Solution Approach 2:
The system uses partial finetuning by training the model on a selective subset of the most relevant and impactful feedback data rather than processing all available feedback at once. This partial action approach reduces the immediate processing time while still achieving significant domain adaptation. The system can prioritize feedback based on its perceived importance or frequency.
Data Source
AI summary
A method and system for finetuning automated sentiment classification by at least one processor may include: receiving a first machine learning (ML) model M0, pretrained to perform automated sentiment classification of utterances, based on a first annotated training dataset; associating one or more instances of model M0 to one or more corresponding sites; and for one or more (e.g., each) ML model M0 instance and/or site: receiving at least one utterance via the corresponding site; obtaining at least one data element of annotated feedback, corresponding to the at least one utterance; retraining the ML model M0, to produce a second ML model M1, based on a second annotated training dataset, wherein the second annotated training dataset may include the first annotated training dataset and the at least one annotated feedback data element; and using the second ML model M1, to classify utterances according to one or more sentiment classes.