Self-learning Dialogue Apparatus for Incremental Knowledge Augmentation
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Solution Overview
Problem
Existing dialogue systems face challenges in updating and enhancing their knowledge to recognize and respond to user inputs effectively, leading to high costs and time consumption, especially when dealing with new user requirements or misunderstood intentions.
Innovation Solution
An incremental self-learning based dialogue apparatus and method that includes a dialogue processing unit, a dialogue establishment unit, and a self-knowledge augmentation unit, which automatically learns and updates the knowledge base by analyzing user interactions, detecting errors, and annotating intentions, thereby enhancing the system's ability to recognize and respond to user inputs in real time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If dialogue knowledge is established by developers or experts in application field, then the system can respond appropriately to user utterances within the established field, but the cost, storage space, and development time are limited, and the application field of the dialogue system is limited
Solution Approach 1:
The dialogue system automatically learns from user interactions and updates its own knowledge base without requiring manual intervention from developers or experts. The self-learning module collects dialogue logs, analyzes user intentions, and incrementally updates the knowledge base, enabling the system to serve itself in expanding its capabilities.
Solution Approach 2:
The system performs preliminary analysis of dialogue logs and user intentions before formally updating the knowledge base. By pre-processing and categorizing user inputs, the system prepares learning materials in advance, making the knowledge update process more efficient and targeted.
2Adaptability or versatility
If developers and knowledge experts develop massive dialogue knowledge in consideration of various situations, then the system can handle various user utterances, but much time and cost are needed for reflecting new user requirements
Solution Approach 1:
The knowledge base is designed to be dynamic and continuously updateable. Instead of static knowledge established during development, the system dynamically adapts its knowledge base by incorporating new user interactions in real-time, allowing the dialogue coverage to expand without fixed time constraints.
Solution Approach 2:
The system implements a feedback loop where user interactions are continuously monitored, analyzed, and used to update the knowledge base. This feedback mechanism enables the system to learn from actual usage patterns and automatically incorporate new requirements, reducing the time needed to adapt to changing user needs.
3Adaptability or versatility
If a dialogue system capable of recognizing speeches of all users is designed at initial stage, then it can understand speeches of users which are not frequently used, but much time and cost are consumed at initial stage
Solution Approach 1:
The system segments the knowledge base into core established knowledge and dynamically learned knowledge. This segmentation allows the initial system to remain relatively simple while providing a framework for incremental expansion through self-learning, reducing initial complexity while maintaining future adaptability.
Data Source
AI summary
An incremental self-learning based dialogue apparatus for dialogue knowledge includes a dialogue processing unit configured to determine a intention of a user utterance by using a knowledge base and perform processing or a response suitable for the user intention, a dialogue establishment unit configured to automatically learn a user intention stored in a intention annotated learning corpus, store information about the learned user intention in the knowledge base, and edit and manage the knowledge base and the intention annotated learning corpus, and a self-knowledge augmentation unit configured to store a log of a dialogue performed by the dialogue processing unit, detect and classify an error in the stored dialogue log, automatically tag a user intention for the detected and classified error, and store the tagged user intention in the intention annotated learning corpus.