Neural Machine Translation Model Training via Code Fragmentation

The patent segments programming language statements into smaller sub-statements or phrases to create training data pairs. This segmentation approach allows the NMT model to learn from finer-grained translation units, improving training data quality while maintaining translation efficiency. The source code is divided into meaningful segments that can be paired with corresponding target language segments for training.

The patent performs preliminary processing of source and target code to generate training data pairs before actual NMT model training. This includes extracting statement pairs, segmenting code, and preparing aligned training examples in advance. This preliminary action ensures high-quality training data is ready before model training begins, resolving the training data quality issue while maintaining efficient translation.

The patent replaces the mechanical rule-based translation system with a neural machine translation model trained on segmented programming language data. Instead of using complex hand-crafted translation rules, the system uses learned patterns from training data to perform translations. This substitution reduces system complexity while maintaining translation accuracy through the neural model's ability to learn from examples.

The patent changes the approach from fixed translation rules to dynamic neural network parameters that are learned from training data. The translation accuracy is achieved not through predetermined rules but through optimized neural network weights and parameters. This parameter-based approach reduces the complexity of manually managing translation rules while maintaining or improving translation quality.

The patent segments large volumes of source and target code into smaller statement pairs or phrase pairs to create training data. This segmentation automatically generates structured training examples from bulk code corpora, reducing the manual processing effort required. The segmented units are easier to align and process, enabling efficient model training with large datasets without proportionally increasing data processing complexity.

The system automatically generates training data pairs from source and target code corpora through automated segmentation and pairing processes. This self-service approach eliminates the need for manual annotation or curation of training data, allowing bulk volume data to be processed efficiently. The automated pipeline transforms raw code pairs into ready-to-use training examples, reducing data processing complexity while maintaining model efficiency.