Dialogue State Tracking Model Feature Integration
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Solution Overview
Problem
Conventional dialogue management technologies, both rule-based and learning-based, face challenges in accurately tracking dialogue states and completing tasks due to limitations in semantic analysis and feature selection for neural network training, leading to inaccuracies and inflexibility.
Innovation Solution
An apparatus and method that retrieve field features and candidate-term features from a database to generate integrated features, relation sub-sentences, and sentence relation features, which are then used to train a dialogue state tracking model, incorporating advanced relation and semantic features to improve accuracy and task completion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If rule-based dialogue management technologies are used, then the system has clear operational rules and structure, but the system lacks flexibility and cannot handle misjudgments or continuous errors
Solution Approach 1:
The patent segments the dialogue management system into multiple independent modules: rule-based processing module, machine learning module, and error correction module. This segmentation allows each module to handle specific aspects of dialogue management independently, combining the structure of rule-based systems with the flexibility of machine learning for error handling and adaptation.
2Adaptability or versatility
If learning-based dialogue management technologies are used, then the system improves flexibility and adaptability, but the accuracy of dialogue state tracking remains insufficient due to inadequate features
Solution Approach 1:
The patent merges multiple feature types (semantic features, relation features, candidate term features) into a comprehensive feature set for training the dialogue state tracking model. This combination of diverse features enhances both the flexibility of the learning-based system and its accuracy in tracking dialogue state by providing more informative input to the model.
Solution Approach 2:
The patent creates a composite feature representation by integrating different types of linguistic and semantic features (syntax, semantics, relations, candidate terms) into a unified feature vector. This composite feature structure enables the machine learning model to leverage multiple aspects of dialogue information simultaneously, improving tracking accuracy while maintaining adaptability.
3Ease of manufacture
If conventional learning-based technologies train neural network models with basic features, then the training process is simple, but the model cannot accurately track dialogue state and complete tasks
Solution Approach 1:
The patent performs preliminary feature engineering and selection before training the neural network model. By pre-processing dialogue data to extract and organize multiple types of features (semantic, relational, candidate terms) in advance, the system simplifies the training process while providing comprehensive input that enables accurate dialogue state tracking and task completion.
Data Source
AI summary
An apparatus and method for generating a dialogue state tracking model. The apparatus retrieves a field feature corresponding to a queried field from a database according to the queried field corresponding to a queried message. The apparatus retrieves a candidate-term feature corresponding to each of at least one candidate-term corresponding to the queried field from the database, and integrates them into an integrated feature. The apparatus also generates at least one relation sub-sentence of a reply message corresponding to the queried message and generates a sentence relation feature according to the at least one relation sub-sentence. The apparatus further generates a queried field related feature according to the field feature, the integrated feature and the sentence relation feature and trains the dialogue state tracking model according to the queried field related feature.