Dialog State Determination via Scenario Vector Intermediary
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Solution Overview
Problem
Current dialog state determination methods, such as those using LSTM neural networks, face challenges in accurately understanding user intent due to limited semantic understanding and unclear expressions, leading to low accuracy in determining the correct response mode in dialog systems.
Innovation Solution
The method involves obtaining dialog information, determining target scenario information, and constructing scenario vectors to represent association relationships, which are then used to determine the dialog state, improving accuracy by accounting for different dialog scenarios and reducing interference from multiple interpretations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If LSTM neural network is used to determine dialog state based on semantic understanding, then the system can automatically process dialog information, but the accuracy of dialog state determination is low due to limited semantic understanding ability and unclear user expressions
Solution Approach 1:
The patent introduces scenario information as an intermediary element between the user's dialog input and the dialog state determination. The scenario information captures contextual details about the dialog situation (such as current activity, environment, or user state), which helps disambiguate unclear expressions and compensates for limited semantic understanding. This intermediary layer enriches the input to the LSTM network, improving accuracy without reducing automation.
2Measurement precision
If multiple scenario information are considered to improve understanding of user intent, then the accuracy of dialog state determination improves, but the complexity of the system increases due to scenario vector construction and management
Solution Approach 1:
The patent merges multiple scenario information elements into a unified scenario vector representation. Instead of handling separate scenario attributes independently, the system combines them into a single integrated vector that captures the overall dialog scenario. This merging reduces the complexity of managing multiple separate scenario components while preserving the comprehensive information needed for accurate intent understanding.
Solution Approach 2:
The patent transforms scenario information from discrete categorical data into continuous vector representations through parameter changes. By converting scenario attributes into numerical vectors that can be processed by the LSTM network, the system enables more flexible and nuanced processing of scenario data, improving the model's ability to understand user intent while maintaining computational efficiency.
Data Source
AI summary
In a method for determining a dialog state, first dialog information is obtained. The first dialog information is dialog information inputted during a dialog process. Based on the first dialog information, target scenario information corresponding to the first dialog information is determined. The target scenario information is used to indicate a dialog scenario of the first dialog information. Based on the first dialog information and the target scenario information, a first dialog state corresponding to the first dialog information is obtained. The first dialog state is used to represent a response mode for responding to the first dialog information.