Dialogue System Data Processing Using Reading Comprehension
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Solution Overview
Problem
Existing task-oriented dialogue systems are complex and require significant developer involvement, high learning and data costs, and low development efficiency due to the need for pipeline-based implementation and extensive training of intention recognition models.
Innovation Solution
A data processing method for a dialogue system that uses a pre-configured task description, reading comprehension techniques, and a pre-generated dialogue flow to extract answers from user interactions, reducing the need for large-scale intention data training and simplifying system construction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If pipeline-based implementation with multiple modules is used, then the dialogue system can accomplish complex dialogue goals, but the system complexity increases and development efficiency decreases
Solution Approach 1:
The patent merges multiple separate modules (intention recognition, dialogue state tracking, response generation) into a unified end-to-end neural network model. This integration maintains the capability to handle complex dialogue tasks while reducing system structural complexity and eliminating the need for separate training of each module.
Solution Approach 2:
The unified dialogue model performs multiple functions (intention recognition, state tracking, response generation) simultaneously through a single architecture. This multi-functionality reduces the number of components needed while maintaining comprehensive dialogue handling capabilities.
2Manufacturing precision
If separate training and setup of each module is required, then each module can be optimized independently, but the development threshold increases and learning cost rises
Solution Approach 1:
By combining all dialogue processing functions into a single end-to-end model, the patent eliminates the need for separate module training and optimization. Developers can train the complete system uniformly, significantly lowering the learning curve and technical barrier while maintaining overall system performance.
3Measurement precision
If extensive intention recognition model training is performed, then the system can accurately understand user intentions, but the data cost increases significantly
Solution Approach 1:
The end-to-end model jointly learns intention recognition and dialogue state tracking from unified training data, eliminating the need for separate large-scale intention recognition training. This integrated approach reduces data requirements while maintaining accurate intention understanding through multi-task learning.
Solution Approach 2:
The model performs self-supervised learning by automatically learning dialogue representations and intentions from raw dialogue data without requiring manually labeled intention datasets. The system leverages the dialogue context itself to supervise the learning process, significantly reducing annotation costs.
Data Source
AI summary
Provided are a data processing method for a dialogue system, an apparatus, a device and a medium. The method includes: obtaining a pre-configured task description, wherein the task description comprises at least one task name and at least one task attribute corresponding to a respective task name; extracting, based on a reading comprehension technique, an answer corresponding to the task description from content of a current dialogue with a user; and completing the dialogue with the user according to the answer and a pre-generated dialogue flow.