Paraphrase Generation with Quality Vectors for Meaning-Diversity Balance
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Solution Overview
Problem
Existing automated paraphrase generation techniques struggle to balance semantic similarity and linguistic diversity, often requiring specific sentence information and limiting scalability and coverage.
Innovation Solution
A machine learning model trained with quality control vectors for semantic similarity, syntactic distance, and lexical distance, allowing direct control over paraphrase quality, using a three-dimensional input vector to generate paraphrases that conform to desired quality constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If linguistic diversity is increased to improve paraphrase quality, then lexical and syntactic differences from the original sentence increase, but it becomes more difficult to preserve sentence meaning
Solution Approach 1:
The patent applies parameter changes by transforming the control approach from discrete syntactic tree constraints to continuous quality control vectors. These vectors represent desired levels of semantic similarity, lexical diversity, and syntactic diversity as continuous parameters, allowing the model to generate paraphrases that satisfy multiple quality dimensions simultaneously through optimized parameter balancing.
Solution Approach 2:
The patent implements dynamics by making the paraphrase generation process adaptive through quality control vectors. The system dynamically adjusts the balance between semantic similarity and linguistic diversity based on the input vector, enabling flexible control over the trade-off between preserving meaning and achieving variety without requiring fixed syntactic constraints.
2Manufacturing precision
If specific sentence information such as parse tree or keywords is provided to control paraphrase generation, then paraphrase quality improves, but device complexity and usage complexity increase
Solution Approach 1:
The patent extracts the essential quality control requirements from complex syntactic constraints and represents them as simplified quality control vectors. Instead of requiring detailed parse trees or keyword lists, the system extracts and controls only the critical quality dimensions (semantic similarity, lexical diversity, syntactic diversity) through compact vector representations.
Solution Approach 2:
The patent changes the control parameters from complex syntactic structures to simple continuous vectors. This parameter transformation reduces the complexity burden while maintaining control effectiveness, as the vectors encode desired quality levels without requiring the model to process detailed syntactic information.
3Manufacturing precision
If specific sentence information such as parse tree or keywords is provided to control paraphrase generation, then paraphrase quality improves, but scalability and coverage are limited
Solution Approach 1:
The patent achieves universality by designing a quality control framework that works across different sentence types and domains through a unified vector interface. The quality control vectors provide a domain-general control mechanism that does not require sentence-specific preprocessing or specialized constraints, enabling broad applicability and scalability.
Solution Approach 2:
The patent extracts and controls only the essential quality dimensions needed for good paraphrases, represented in a universal vector format. This extraction of core quality factors into a standardized representation enables the system to scale across different applications without requiring application-specific constraint formulations.
Data Source
AI summary
A computer-implemented method including: receiving, as input, a dataset comprising training pairs (s, t), wherein each training pair comprises (i) a source sentence s and (ii) a target paraphrase t of the source sentences; at a training stage, training a machine learning model on the dataset, to obtain a trained quality-controlled paraphrase generator model, wherein during the training stage, each of the training pairs is associated with a predicted control vector representing a predicted paraphrase quality of the source sentence in the training pair; and at an inference stage, inferencing the trained quality-controlled paraphrase generator model on an input sentence, wherein the input sentence is associated with an input quality control vector, to obtain an output paraphrase of the input sentence which conforms to the quality control vector.