Multi-user Dialog System Topic Segmentation
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Solution Overview
Problem
Existing spoken dialog systems are limited to single-user scenarios and specific domains, failing to effectively handle multi-user conversations and domain detection in multi-user environments, where context from human-human interactions is not adequately utilized.
Innovation Solution
A multi-user, multi-domain dialog system that employs topic segmentation and conversational context to improve domain detection by analyzing interactions between multiple users, using lexical and contextual features to determine relevant context and reduce domain detection errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the system limits itself to single-user scenarios, then the system can maintain simplicity and focus on specific domains, but it cannot handle multi-user conversations effectively
Solution Approach 1:
The system segments the conversation processing by introducing an addressee detection component that identifies which user is speaking to the computer. This segmentation allows the system to handle multi-user scenarios by dividing the conversation stream into distinct user-addressed segments, maintaining clarity and reducing complexity through structured organization of multi-user interactions
2Adaptability or versatility
If the system focuses on limited domains, then domain detection accuracy improves, but the system cannot respond to diverse user requests across multiple domains
Solution Approach 1:
The system performs preliminary domain detection on human-addressed utterances before processing computer-addressed requests. By detecting the domain context from human-human conversation segments, the system prepares domain information in advance that aids subsequent domain detection for computer-addressed requests, improving accuracy across multiple domains without requiring the system to handle all domains simultaneously
3Measurement precision
If the system uses human-addressed context for domain detection, then domain detection accuracy improves, but the system complexity increases due to context management requirements
Solution Approach 1:
The system extracts only the necessary domain-relevant features from human-addressed utterances, rather than processing entire conversation contexts. By taking out and utilizing only the domain detection features from human-human interactions, the system improves domain detection accuracy while minimizing the complexity burden of managing full conversational context
4Measurement precision
If the system requires explicit addressing terms or gestures, then addressee detection accuracy improves, but natural user interaction is compromised
Solution Approach 1:
The system uses self-service through automatic addressee detection that analyzes conversation patterns and contextual cues without requiring users to perform explicit addressing actions. The system serves itself by automatically identifying which user is addressing the computer based on conversation structure and context, eliminating the need for users to speak addressing terms or make gestures, thus maintaining both accuracy and natural interaction
Data Source
AI summary
A dialog system for use in a multi-user, multi-domain environment. The dialog system understands user requests when multiple users are interacting with each other as well as the dialog system. The dialog system uses multi-human conversational context to improve domain detection. Using interactions between multiple users allows the dialog system to better interpret machine directed conversational inputs in multi-user conversational systems. The dialog system employs topic segmentation to chunk conversations for determining context boundaries. Using general topic segmentation methods, as well as the specific domain detector trained with conversational inputs collected by a single user system, allows the dialog system to better determine the relevant context. The use of conversational context helps reduce the domain detection error rate, especially in certain domains, and allows for better interactions with users when the machine addressed turns are not recognized or are ambiguous.