Discourse Labeling Model for Internet Question Answering
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Solution Overview
Problem
Current internet question answering systems lack the ability to provide intelligent and user-friendly question answering experiences, as they fail to accurately detect discourse boundaries and adapt to user interactions, leading to incomplete or conflicting responses.
Innovation Solution
An internet question answering system and method that learns and trains a discourse label model using user dialog logs to label questions as 'discourse continue' or 'discourse end' in real-time, allowing for boundary clearance, correction, and model adjustment based on user interactions, ensuring accurate and relevant responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a discourse labeling model is used to label user questions as 'discourse continue' or 'discourse end' in real-time, then the system can accurately detect discourse boundaries and provide intelligent question answering, but the device complexity and computational resources increase
Solution Approach 1:
The discourse labeling model is pre-trained offline using collected user dialog logs and labeled data. This preliminary action prepares the model in advance, allowing it to perform accurate real-time discourse boundary detection without adding computational burden during online operation. The model learns discourse patterns and labeling rules beforehand, enabling fast inference when users actually interact with the system.
Solution Approach 2:
The system automatically collects user dialog logs, generates labeled data through the labeling model, and performs iterative optimization without requiring manual intervention. The discourse labeling model self-adjusts by comparing its predictions with actual user behavior patterns, automatically improving its accuracy over time while reducing the need for complex external configuration or monitoring.
2Adaptability or versatility
If the system collects and uses user dialog logs for model training, then the adaptability to user interactions improves, but the loss of user privacy and security risks increase
Solution Approach 1:
The system extracts only the necessary linguistic features and discourse patterns from user dialog logs, rather than storing or processing complete raw conversations. By taking out only the essential information needed for training the discourse labeling model (such as question structures, discourse markers, and interaction patterns), the system reduces privacy exposure while maintaining training effectiveness.
Solution Approach 2:
The discourse labeling model acts as an intermediary layer between raw user dialog data and the question answering system. Instead of directly using raw dialog logs which contain sensitive information, the model processes and transforms them into anonymized discourse labels and features, serving as a privacy-protecting mediator that preserves user identity while enabling adaptive learning.
3Productivity
If the system performs online and real-time discourse labeling, then the response speed and user experience improve, but the energy consumption and computational load increase
Solution Approach 1:
The computationally intensive model training and optimization are performed in advance during offline periods, allowing the system to use pre-computed models during real-time interactions. This preliminary action shifts the energy consumption from the high-speed response phase to the offline training phase, enabling fast real-time labeling with minimal computational burden.
Solution Approach 2:
The system applies discourse labeling selectively based on the needs of the question answering task, rather than processing every user input with the full model. By performing partial labeling only when necessary for discourse boundary detection, the system reduces unnecessary computational energy consumption while maintaining adequate response speed for critical functions.
Data Source
AI summary
An internet question answering method is applied in an internet question answering system. A current-turn user question is received. Based on a discourse labeling model, a discourse of the current-turn user question is labeled as “discourse continue” or “discourse end” online and real-time. Based on the discourse of the current-turn user question, whether to clear at least one discourse of an old boundary is determined. The internet question answering system answers the current-turn user question. A match degree between a next-turn user question and the segment of the dialog log is compared to determine whether the discourse of the current-turn user question is correct, whether to correct the discourse of the current-turn user question, and whether to adjust the discourse labeling model by using the current-turn user question.