Neural Dependency Tree for Natural Language Generation
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Solution Overview
Problem
Existing natural language generation systems using RNN models struggle to effectively convey various grammatical structures, leading to limitations in generating natural and meaningful sentences.
Innovation Solution
A natural language generating apparatus that uses a trained neural network model to generate a dependency tree representing the syntax structure of words, employing traversal algorithms like BFS and DFS to determine node values and generate sentences, allowing for more effective reflection of grammatical structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a rule-based acoustic model or deep neural network-based acoustic model is used for natural language generation, then the system can generate natural language sentences, but the system struggles to effectively convey various grammatical structures
Solution Approach 1:
The patent segments the natural language generation process into distinct components: semantic expression information processing, dependency tree generation, and sentence generation. The dependency tree structure divides grammatical relationships into hierarchical nodes and edges, allowing separate processing of syntactic structures while maintaining overall sentence coherence. This segmentation enables the system to handle complex grammatical structures more effectively.
Solution Approach 2:
The patent introduces a dependency tree as an intermediary structure between semantic expression information and final natural language sentences. This intermediate representation captures grammatical relationships explicitly through nodes (words) and edges (relationships), serving as a bridge that translates semantic information into grammatically correct sentences while preserving syntactic structure.
2Adaptability or versatility
If traversal algorithms like BFS and DFS are used to determine node values in dependency trees, then diverse sentence structures can be generated, but the system complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network model on dependency tree structures before actual sentence generation. The model learns to predict node values and relationships in advance during the training phase, so that during inference, the traversal algorithms can efficiently generate diverse sentences without requiring complex real-time computations. This pre-learning reduces the computational burden during actual operation.
3Reliability
If a trained neural network model generates dependency trees based on semantic expression information, then more natural and varied sentences can be produced, but the training process requires visiting and processing numerous nodes in training dependency trees
Solution Approach 1:
The patent applies partial action by focusing the training process on specific aspects of dependency trees rather than exhaustively processing every possible node configuration. The neural network model is trained to predict key node values and relationships that are most critical for sentence naturalness, rather than attempting to master all possible grammatical structures equally. This selective training approach reduces training time while maintaining sentence quality.
Data Source
AI summary
A natural language generating apparatus includes: a receiver configured to receive semantic expression information for generating a natural language sentence; and a controller configured to: generate a dependency tree representing a syntax structure of at least one word determined based on the received semantic expression information, based on a trained neural network model, and to generate the natural language sentence based on the generated dependency tree.