Conversational Intent Disambiguation via Ambiguity Index
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conversational systems face challenges in disambiguating user intent when user input is lexically or semantically ambiguous, leading to multiple qualifying responses that are not easily resolved.
Innovation Solution
The method employs structural knowledge, user preferences, location, and time to determine an ambiguity index, allowing the system to ask clarifying questions or provide disambiguating responses that are natural and intuitive, similar to human conversation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the system provides multiple qualifying responses for ambiguous user input, then the system appears comprehensive and thorough, but the user experience becomes overwhelming and the system fails to understand user intent
Solution Approach 1:
The system performs preliminary disambiguation by calculating an ambiguity index for each content item before presenting responses. This preliminary action filters and ranks potential responses based on their ambiguity characteristics, allowing the system to proactively select the most appropriate response rather than reacting after receiving user feedback
Solution Approach 2:
The system applies local quality by differentiating the treatment of individual content items based on their specific ambiguity characteristics. Each content item is evaluated for its ambiguity index, and responses are selectively filtered and ranked according to their individual ambiguity properties, rather than treating all responses uniformly
2Measurement precision
If the system asks clarifying questions to resolve ambiguity, then the system can accurately understand user intent, but the conversation becomes longer and more complex
Solution Approach 1:
The system performs self-service by autonomously calculating ambiguity indices and making disambiguation decisions without requiring user clarification. The system independently evaluates content items, computes their ambiguity scores, and selects appropriate responses based on pre-established criteria, eliminating the need for user intervention in the disambiguation process
Solution Approach 2:
The system changes the parameter of response selection by using ambiguity index as a filtering criterion. Instead of presenting all matching responses or requiring clarification, the system transforms the selection process into a parameter-based decision, choosing responses based on their ambiguity indices and other content-specific parameters
3Reliability
If the system uses complex disambiguation algorithms, then the system can handle semantic ambiguity effectively, but the system complexity increases
Solution Approach 1:
The system applies segmentation by dividing the disambiguation process into separate, manageable components: content item retrieval, ambiguity index calculation, and response selection. This segmentation allows each component to be optimized independently and simplifies the overall system architecture while maintaining effective disambiguation capabilities
Solution Approach 2:
The ambiguity index serves as an intermediary mechanism that mediates between the raw content items and the final response selection. This intermediary parameter simplifies the decision-making process by providing a single, comparable metric that captures the essential ambiguity characteristics of each content item, reducing the need for complex multi-factor analysis
Data Source
AI summary
A method of disambiguating user intent in conversational interactions for information retrieval is disclosed. The method includes providing access to a set of content items with metadata describing the content items and providing access to structural knowledge showing semantic relationships and links among the content items. The method further includes providing a user preference signature, receiving a first input from the user that is intended by the user to identify at least one desired content item, and determining an ambiguity index of the first input. If the ambiguity index is high, the method determines a query input based on the first input and at least one of the structural knowledge, the user preference signature, a location of the user, and the time of the first input and selects a content item based on comparing the query input and the metadata associated with the content item.