Neural Paragraph Generation Model for Text Sequences
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Solution Overview
Problem
Conventional natural language generation methods require significant time and labor to create and check rules for arranging sentences, making it difficult to generate coherent paragraphs efficiently.
Innovation Solution
A method and apparatus using a recurrent neural network-based paragraph generation model with an encoder-decoder structure, incorporating units like LSTM or GRU, and mechanisms such as attention and copy mechanisms to generate sentence IDs and corresponding sentences from input data, reducing the need for manual rule creation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional rule-based methods are used to determine sentence arrangement and generate sentences, then the generation process follows preset rules, but it requires significant time and labor to create and check rules for all cases
Solution Approach 1:
The patent replaces the mechanical rule-based system with a neural network-based semantic analysis system. The semantic analysis unit uses machine learning models to automatically understand and generate sentence arrangements without requiring manual rule creation, thereby eliminating the time-consuming rule development and verification process while maintaining generation quality
Solution Approach 2:
The system enables self-service by allowing the semantic analysis unit to automatically learn and adapt to different data types and sentence structures through training. The model continuously improves its sentence arrangement capabilities by learning from training data, eliminating the need for external experts to create and maintain complex rule sets
2Ease of manufacture
If preset rules are used for sentence generation, then the process is systematic, but it is very difficult to create rules with regard to all cases
Solution Approach 1:
The patent changes the fundamental parameter of sentence generation from fixed preset rules to dynamic neural network parameters. The semantic analysis unit adjusts its internal parameters and weights based on training data, enabling it to adapt to various sentence structures and data types without requiring separate rule sets for each case
Solution Approach 2:
The neural network-based semantic analysis unit is designed to be universal, handling multiple types of data and sentence structures through a single unified model. The system can process news articles, reports, and other text types using the same underlying architecture, eliminating the need for case-specific rule development
3Manufacturing precision
If manual rule creation is used for sentence arrangement, then the rules can be precisely controlled, but much time and labor are needed in checking errors from the created rules
Solution Approach 1:
The patent implements feedback mechanisms where the generated sentences are automatically evaluated and used to refine the semantic analysis model. The system provides feedback on generation quality and uses this information to improve future sentence arrangements, eliminating the need for manual error checking while maintaining high precision through continuous learning
Data Source
AI summary
Disclosed are an apparatus and method for generating a natural language. According to an embodiment of the present disclosure, the method for generating the natural language includes receiving a plurality of pieces of data, generating a sentence ID sequence including one or more sentence IDs from the plurality of pieces of data, and generating a sentence corresponding to each of the sentence IDs included in the sentence ID sequence from the sentence ID sequence and the plurality of pieces of data.