Semantic Disambiguation via Historical Context in Dialog Systems
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Solution Overview
Problem
In dialog systems, users often input sentences that omit keywords, making it difficult for the system to understand their intentions, particularly in multi-field, multi-round interactions where ambiguity is prevalent, and existing methods rely on large-scale annotated corpora and syntax analysis, which are inefficient and inaccurate.
Innovation Solution
A semantic disambiguation method that performs semantic analysis on user input to determine if a service field is ambiguous, and if so, uses historical service fields from previous dialog messages to disambiguate the current field, employing context-based strategies to identify the intended service field.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If semantic analysis is performed on user input to determine service field ambiguity, then the accuracy of understanding user intention is improved, but the processing time and computational complexity increase
Solution Approach 1:
The system performs semantic analysis and ambiguity detection as preliminary actions before full dialog processing. By identifying ambiguous service fields early in the interaction, the system can proactively request clarification or use context from previous turns, avoiding costly reprocessing later and reducing overall processing time while maintaining high accuracy.
Solution Approach 2:
The system uses its own historical dialog data and context information to resolve ambiguities without requiring external resources or large-scale annotated corpora. By leveraging self-generated context from the conversation history, the system efficiently disambiguates service fields using already-available information, reducing computational overhead.
2Reliability
If context-based disambiguation is used to resolve service field ambiguity, then the reliability of intention recognition is improved, but the device complexity increases
Solution Approach 1:
The patent extracts only the necessary contextual information from historical dialog messages that is relevant to resolving the current ambiguity, rather than processing the entire conversation history. This selective extraction of pertinent context improves reliability while reducing the computational complexity associated with analyzing all available data.
Solution Approach 2:
The system introduces an intermediary disambiguation module that sits between intent recognition and dialog processing. This intermediary component specialized in context-based disambiguation handles the complex task of resolving ambiguities using historical service fields, isolating the complexity to a dedicated module rather than distributing it throughout the entire system.
3Productivity
If historical service fields are used to disambiguate current service field, then the productivity of dialog processing is improved, but the loss of information from outdated context may occur
Solution Approach 1:
The system dynamically adjusts which historical service fields are used for disambiguation based on the current dialog context and temporal relevance. Rather than statically relying on all historical data, the system adaptively selects the most relevant historical contexts, weighting them according to their recency and applicability to the current ambiguity, thus improving productivity while minimizing information loss.
Data Source
AI summary
The present disclosure provides a semantic disambiguation method, a semantic disambiguation device, a server and a storage medium. The method includes the following. Semantic analysis is performed on a current dialog message input by a user. It is determined, based on an analysis result, whether a current service field corresponding to the current dialog message is an ambiguous field. When the current service field is the ambiguous field, disambiguation is performed on the current service field based on one or more predetermined historical service fields corresponding to one or more historical dialog messages input by the user.