Online Dialogue Model Training via Reinforcement Learning
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Solution Overview
Problem
Existing dialogue systems face challenges in maintaining model effectiveness after deployment, as offline-trained models cannot be updated, leading to deviations from real-user interactions and high costs and errors in manual data labeling.
Innovation Solution
A method for training models in dialogue systems that involves acquiring user interaction data, using a combination of intention recognition, recurrent neural networks, and named entity recognition models to predict user intentions and machine behaviors, and performing online model optimization using reinforcement learning without the need for manually labeled data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If offline-trained models are used in dialogue systems, then initial model effectiveness is achieved, but model effectiveness deteriorates over time due to inability to update
Solution Approach 1:
The patent transforms the static offline-trained model into a dynamic system that can continuously update itself through online reinforcement learning. The model transitions from a fixed state to an adaptive state where parameters are continuously optimized based on real-time user feedback, resolving the contradiction between initial effectiveness and long-term adaptability.
Solution Approach 2:
The system implements self-service through automated reinforcement learning that uses user feedback to autonomously update model parameters without requiring manual intervention. The dialogue system trains itself continuously by processing real-world interactions, eliminating the need for periodic offline retraining and maintaining effectiveness over time.
2Measurement precision
If manual data labeling is used for model training, then training data quality is improved, but labeling costs and errors increase
Solution Approach 1:
The system eliminates manual labeling by implementing self-service through reinforcement learning from user feedback. The model automatically learns from real user interactions without requiring human annotators to label data, thereby maintaining training quality while eliminating labeling costs and associated errors.
Solution Approach 2:
The patent incorporates feedback loops where user responses to machine behaviors are automatically collected and used to update model parameters through reinforcement learning. This feedback mechanism replaces manual labeling by using actual user interactions as training signals, achieving high data quality without human intervention.
3Productivity
If offline model training is performed, then initial model performance is achieved, but adaptability to real-user scenarios deteriorates
Solution Approach 1:
The patent transforms the static offline training approach into a dynamic online learning system. The model continuously adapts to real-user scenarios by processing feedback from actual interactions and updating parameters in real-time, thereby maintaining high performance while achieving adaptability to evolving user needs.
Solution Approach 2:
The system performs preliminary offline training to achieve initial performance, then continuously performs preliminary adaptations through online reinforcement learning before deployment. This dual approach ensures both initial performance and ongoing adaptability by preparing the model in advance with both offline pretraining and continuous online adjustments.
Data Source
AI summary
A method of training a model, an electronic device, and a storage medium are provided, which relate to a field of artificial intelligence, and in particular to a field of reinforcement learning, NLP, etc. The method includes: acquiring a dialogue information; obtaining a predicted information based on the dialogue information by using a first intention recognition model, a first recurrent neural network and a first named entity recognition model; obtaining a machine behavior information based on the predicted information by using a first behavior decision model; acquiring a feedback information for the machine behavior; storing at least one of the predicted information, the machine behavior information, or the feedback information as training data in a database; and performing a model optimization training online based on the training data by using a reinforcement learning algorithm, in response to an amount of the training data reaching a preset data amount.