Feature Extraction Model Training for Conversation Semantic Accuracy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing feature extraction models in service scenarios like privacy protection and intelligent conversation struggle to accurately reflect semantic information from conversation contents, leading to suboptimal service processing results due to deviations in feature representation.
Innovation Solution
A model training method that determines target conversation content features by comparing them to context-based features within historical conversations, training the feature extraction model to minimize deviations between these features, ensuring accurate semantic representation and similar feature extraction for conversation contents with similar meanings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a feature extraction model is used to extract features from conversation contents, then service processing can be performed, but the extracted features may deviate from the actual semantic information leading to inaccurate service processing results
Solution Approach 1:
The patent implements feedback by training the model using the deviation between predicted features (from context) and actual features (from target conversation content) as a loss function. The model continuously adjusts its parameters based on this feedback signal to minimize the deviation, thereby improving both feature extraction accuracy and service processing reliability
Solution Approach 2:
The patent changes the parameters of the feature extraction model through iterative training. By adjusting model parameters to minimize the loss function (deviation between first and second features), the model transforms from an initial state with poor accuracy to a trained state with high accuracy in both feature extraction and service processing
2Measurement precision
If the model is trained to minimize deviation between context-based features and target content features, then semantic representation accuracy improves, but training complexity and computational resources increase
Solution Approach 1:
The patent segments the feature extraction task into two distinct components: context-based feature extraction (first feature) and target content-based feature extraction (second feature). This segmentation allows the model to learn different aspects of feature representation separately, improving semantic accuracy while maintaining manageable training complexity through modular architecture
Solution Approach 2:
The patent introduces a loss function as an intermediary mechanism that bridges the gap between context-based predictions and target content features. This intermediary translates the semantic accuracy requirement into a computable optimization objective, enabling the model to learn accurate semantic representations without directly implementing complex training procedures
Data Source
AI summary
The present specification discloses a model training method and apparatus, a service processing method and apparatus, a storage medium, and a device. The model training method includes: obtaining a historical conversation; determining a target conversation content from the historical conversation; inputting the historical conversation into a to-be-trained feature extraction model for the feature extraction model to determine a conversation content feature corresponding to the target conversation content as a first feature based on a conversation content other than the target conversation content in the historical conversation, and to determine a conversation content feature corresponding to the target conversation content as a second feature based on the target conversation content; and training the feature extraction model with an optimization goal of reducing a deviation between the first feature and the second feature, where the trained feature extraction model is used to determine an output conversation content feature corresponding to each input conversation content, and send the output conversation content feature for a receiving end to perform service processing based on the received output conversation content feature.