Sentence Conversion System Using Machine Learning
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Solution Overview
Problem
Existing sentence conversion systems, such as those described in Non-Patent Literatures 1 to 4, face challenges in accurately converting declarative sentences into question sentences without requiring extensive manual rule definition, which is time-consuming and effort-intensive.
Innovation Solution
A sentence conversion system that utilizes a processor to create converters based on machine translation methods, such as statistical or neural machine translation, to associate declarative sentences with question sentences, allowing for automatic conversion without the need for manual rule definition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual rule definition is used for sentence conversion, then conversion accuracy can be improved, but time consumption and effort increase significantly
Solution Approach 1:
The patent replaces the manual mechanical rule-definition process with an automated machine learning system. The machine learning model automatically learns sentence conversion patterns from training data, eliminating the need for manual rule creation while maintaining high conversion accuracy between sentence types (e.g., declarative to question sentences).
Solution Approach 2:
The patent changes the approach from fixed manual rules to dynamic machine learning models that can adapt to different sentence structures. The model learns from training data and automatically adjusts its conversion parameters, enabling accurate sentence type conversion without manual rule specification for each case.
2Measurement precision
If manual rule definition is used for sentence conversion, then conversion accuracy can be improved, but the complexity of rule definition increases
Solution Approach 1:
The patent replaces the complex manual rule-definition system with an automated machine learning model. The model automatically captures conversion patterns from training data, eliminating the need for complex manual rule specification while maintaining high accuracy in sentence type conversion.
Solution Approach 2:
The machine learning model performs self-learning from training data, automatically defining conversion rules without human intervention. The model processes training sentences and autonomously extracts conversion patterns, replacing the complex manual rule-creation process with automated self-organization of knowledge.
3Measurement precision
If a large number of rules are prepared for sentence conversion, then conversion accuracy increases, but the time and effort required for rule definition increases
Solution Approach 1:
The patent changes from a rule-based approach requiring many manually defined rules to a machine learning approach where the model learns conversion patterns from training data. This parameter change enables the system to achieve high conversion accuracy across multiple sentence types without requiring extensive manual rule preparation, significantly improving productivity.
Solution Approach 2:
The patent substitutes the manual rule-preparation process with automated machine learning. The machine learning model automatically processes training data and generates conversion capabilities, eliminating the time-consuming manual rule definition process while maintaining or improving conversion accuracy across diverse sentence structures.
Data Source
AI summary
A sentence conversion system includes at least one processor that obtains data including a first type sentence and a second type sentence in association with each other, the second type sentence being a sentence obtained by converting the first type sentence into a second type in a same language, creates at least one converter that converts a sentence type in the same language based on the data and a machine translator that translates a first language into a second language, inputs the first type sentence in the at least one converter, and obtains a sentence converted by the at least one converter into the second type in the same language.