Dialogue System Extended Domain Natural Language Recognition
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Solution Overview
Problem
Current dialogue systems face challenges in understanding and responding to natural language inputs from humans, as they often rely on predefined domains that fail to recognize variations in user inputs, leading to decreased natural language recognition rates.
Innovation Solution
A dialogue system that uses an extended domain, generated in real-time or pre-defined, by parsing user voice data into syllables and applying associated language data such as synonyms, hyponyms, and hypernyms to expand the input set, with weight restrictions to manage domain size, allowing for improved recognition of user language.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a predefined domain is used for natural language recognition, then the system structure remains simple and manageable, but the natural language recognition rate deteriorates when user inputs vary from predicted patterns
Solution Approach 1:
The domain is transformed from a static predefined structure to a dynamic structure that adapts during dialogue. The system generates an extended domain in real-time based on the initial domain and actual user inputs, allowing the domain to evolve and accommodate varying user language patterns while maintaining a manageable base structure.
Solution Approach 2:
The system performs preliminary domain extension by generating an extended domain before actual dialogue processing. This extended domain is created by applying language data (synonyms, hyponyms, hypernym) to the initial domain, preparing the system to handle various user input variations in advance while keeping the base domain simple.
2Reliability
If the domain is extended to accommodate more user input variations, then the natural language recognition rate improves, but the domain size and processing complexity increase
Solution Approach 1:
Instead of uniformly expanding the entire domain, the system applies local extension by generating an extended domain only for specific input sets that are encountered during dialogue. The extension is localized to relevant words and phrases, applying language data selectively to accommodate user variations without unnecessarily expanding the overall domain size.
Solution Approach 2:
The system generates an extended domain that may be larger than strictly necessary (excessive action), but uses weighting mechanisms to prioritize and process only the most relevant extended input sets. This allows the system to prepare comprehensively while managing processing load by focusing on high-weight items.
3Adaptability or versatility
If language data such as synonyms, hyponyms, andhypernym is applied to extend the input set, then the system's ability to understand user language improves, but the processing time and computational resources increase
Solution Approach 1:
The system performs domain extension in advance by generating the extended domain before actual dialogue processing. This preliminary action includes applying language data to create extended input sets, so that when user inputs are processed, the system can quickly match against the pre-prepared extended domain without performing extensive language data processing in real-time.
Solution Approach 2:
The system dynamically manages the extended domain by applying weights to extended input sets and selectively processing only those that meet certain criteria. This dynamic approach allows the system to balance between comprehensive language understanding and processing efficiency, focusing computational resources on the most relevant extended inputs.
Data Source
AI summary
A dialog system uses an extended domain in order to have a dialog with a user using natural language. If a dialog pattern actually input by the user is different from a dialog pattern predicted by an expert, an extended domain generated in real time based on user input is used and an extended domain generated in advance is used to have a dialog with the user.