Dialogue Graph Generation for Task-Oriented Dialogue Systems
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Solution Overview
Problem
Existing task-oriented dialogue systems struggle to enhance response prediction performance without human intervention and fail to efficiently model various dialogue flows based on dialogue datasets.
Innovation Solution
A task-oriented dialogue method and system that utilize a dialogue graph generated by a dialogue graph generation model to enhance response prediction performance. The system generates a dialogue graph modeling conditional relationships for a dialogue dataset, adjusts dialogue act groups based on this graph, and selects the most appropriate dialogue act group to respond to user inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a dialogue graph is created directly by a person or using a rule-based system, then the dialogue model can be constructed, but human intervention is required and various dialogue flows cannot be efficiently modeled
Solution Approach 1:
The system enables automatic dialogue graph construction where the dialogue graph generation model autonomously learns conditional relationships from dialogue datasets without human intervention. The model self-adjusts to model various dialogue flows by processing training data and generating the graph structure automatically, eliminating the need for manual creation while maintaining adaptability to different dialogue scenarios.
Solution Approach 2:
A dialogue graph generation model serves as an intermediary between raw dialogue datasets and the final dialogue model. This intermediate component automatically extracts and structures conditional relationships from unstructured dialogue data, transforming it into a usable dialogue graph that guides the dialogue model's behavior without requiring direct human intervention in the graph construction process.
2Productivity
If multiple AI models and deep learning are used to process large amounts of data, then comprehensive task processing is achieved, but operational cost and time become considerable
Solution Approach 1:
The system performs preliminary action by pre-training the dialogue graph generation model on dialogue datasets to learn conditional relationships in advance. This pre-processing step creates a structured dialogue graph that captures essential patterns, so that during actual operation, the system can quickly query and execute tasks using the pre-established graph structure rather than processing raw data each time, significantly reducing operational time.
Solution Approach 2:
The system segments the complex task processing into distinct phases: (1) offline training phase where the dialogue graph generation model learns from datasets, and (2) online execution phase where the pre-trained model efficiently processes queries. This segmentation allows computationally intensive learning to occur separately from time-critical operations, improving overall productivity while reducing operational time for actual task execution.
Data Source
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AI summary
A task-oriented dialogue method may be performed by a computing system including a memory and a processor using a dialogue model. The task-oriented dialogue method includes: generating a dialogue graph which models at least one conditional relationship for a dialogue dataset; receiving a user dialogue input; sampling a plurality of dialogue act groups for responding to the user dialogue input by using a pre-trained dialogue model; adjusting the plurality of dialogue act groups based on the dialogue graph; and selecting any one dialogue act group which satisfies a predetermined condition among the plurality of dialogue act groups.