Multi-Utterance Generation with Immutability Regulation
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Solution Overview
Problem
Conventional natural language processing systems for multi-utterance generation lack control over the quality of generated utterances, requiring manual efforts and failing to ensure contextual relevance.
Innovation Solution
A processor-implemented method and system for generating multiple context-related utterances with immutability regulation and punctuation memory, which involves converting non-text inputs to text, processing text data to maintain immutability and punctuation consistency, and iteratively generating utterances based on context-related synonyms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional blackbox approaches are used for utterance generation, then the process is simple to implement, but quality control over generated utterances is lost
Solution Approach 1:
The utterance generation process is divided into distinct modular components: input data reception module, non-text to text conversion module, text processing module with immutability regulation, punctuation memory module, iterative utterance generation module, ranking module, and selection module. Each module performs a specific function, enabling quality control at each stage while maintaining systematic organization.
Solution Approach 2:
The system performs preliminary processing of input text data before utterance generation, including tokenization with immutability regulation, identification of context-related synonyms, and punctuation normalization. These preliminary actions ensure that the base data is properly prepared and controlled before the actual generation process begins.
2Productivity
If manual efforts are used for utterance generation, then contextual relevance can be ensured, but productivity is reduced
Solution Approach 1:
The system automatically performs utterance generation without requiring manual intervention. The iterative generation process, ranking based on index of deviation, and selection of high-ranked utterances are all automated functions that operate independently, significantly increasing productivity while maintaining quality through algorithmic control.
Solution Approach 2:
The system implements feedback mechanisms through the ranking process that evaluates generated utterances based on an index of deviation from the original input. This feedback loop ensures that only high-quality, contextually relevant utterances are selected, automatically maintaining contextual relevance without manual review.
3Adaptability or versatility
If multiple context-related utterances are generated, then versatility is improved, but device complexity increases
Solution Approach 1:
The system dynamically generates multiple utterances by iteratively combining context-related synonyms with the tokenized input data. The number and variety of generated utterances can be adjusted based on requirements, providing versatility while the modular architecture manages the complexity of these dynamic operations.
Solution Approach 2:
The system is designed to handle multiple types of input data (text, audio, images, videos) and generate multiple context-related utterances for various applications such as chatbot training and question paper generation. The same core processing modules serve multiple functions across different use cases.
Data Source
AI summary
This disclosure relates to systems and methods for multi-utterance generation of data. Embodiments of the present disclosure utilizes a smart framework that is capable of generating contextually relevant utterances with immutability regulation and punctuation-memory. More Specifically, the present disclosure generates multiple syntactically and semantically correct utterances for text input data in such a way that a provision to selectively maintain or regulate phrases or words intact is provided and punctuation consistency is maintained.