Request Paraphrasing Model for Dialogue Systems
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Solution Overview
Problem
Current dialogue systems struggle to accurately respond to natural language inputs due to their reliance on simple sentence formats, making it difficult to process and classify human-like utterances effectively, especially when requests are phrased differently or contain complex sentences.
Innovation Solution
A request paraphrasing system that includes morphological analysis, word vector conversion, and a machine learning-based request paraphrasing model to convert input requests into more suitable formats for better classification and response, utilizing a neural paraphrasing model and classification model to enhance the probability of obtaining accurate responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a dialogue system uses simple sentence formats for processing, then the system structure remains simple, but the system cannot accurately respond to naturally phrased requests with various expressions
Solution Approach 1:
The patent introduces a request paraphrasing model as an intermediary component between the user input and the dialogue system. This model converts naturally phrased requests into standardized formats that the dialogue system can process accurately, thereby improving response accuracy without requiring the entire system to become complex. The paraphrasing model acts as a mediator that handles the complexity of natural language variation while preserving system simplicity.
Solution Approach 2:
The system performs preliminary paraphrasing of requests before they are processed by the main dialogue system. By pre-converting naturally phrased requests into standardized formats, the system prepares the input data in advance, ensuring that the subsequent processing stages can operate efficiently and accurately without dealing with the full complexity of natural language expressions.
2Reliability
If the system classifies requests using simple criteria, then the classification process is fast, but the classification reliability decreases when handling natural language sentences
Solution Approach 1:
The system performs preliminary classification of requests using simple criteria before applying more complex processing. This preliminary classification quickly identifies obvious cases and routes them appropriately, while only requiring detailed analysis for cases that don't fit simple patterns. This approach maintains fast processing for the majority of requests while improving classification reliability for complex natural language inputs.
Solution Approach 2:
The classification process is segmented into multiple stages: initial simple classification, identification of ambiguous cases, and subsequent detailed analysis only for those ambiguous cases. This segmentation allows the system to maintain fast processing for clear-cut cases while investing more time only when necessary, thereby improving overall classification reliability without proportionally increasing processing time for all requests.
3Adaptability or versatility
If the dialogue system processes various natural language expressions, then the system becomes more versatile, but the probability of giving right answers decreases due to expression variability
Solution Approach 1:
The system changes the parameter of request representation by converting diverse natural language expressions into a standardized format through the paraphrasing model. This parameter transformation preserves the semantic meaning of various expressions while normalizing their structure, allowing the dialogue system to handle diverse inputs versatilely while maintaining high answer accuracy through consistent processing of standardized formats.
Solution Approach 2:
The paraphrasing model serves as a universal converter that handles multiple types of natural language expressions (questions, commands, statements, colloquial phrases) and transforms them all into a standardized request format. This universal approach enables the system to maintain versatility in accepting various expressions while ensuring reliable processing through format standardization.
Data Source
AI summary
A request paraphrasing system 120 allowing a dialogue system to flexibly address to requests in various different manners of expression includes: a pre-processing unit 130 converting a user input 56 to a word vector sequence; and a neural paraphrasing model 94 trained in advance by machine learning to receive the word vector sequence as an input and paraphrasing a request represented by the word vector sequence to a request having a higher probability of obtaining an answer from a question-answering device 122 than the request before paraphrasing. As pre-processing, whether the user input 56 is a request or not may be determined and it may be paraphrased only when it is determined to be a request. Further, a classification model 98 may classify the input request to determine to which request class it belongs, and the classification may be input as one feature to neural paraphrasing model 94.