Vietnamese Machine Translation Model Using Syntactic Information

The patent segments the machine translation model into distinct components: a syntax analysis module that parses sentences into grammatical structures, and a translation module that processes these structured representations. This segmentation allows the syntax analysis to be performed as a separate preprocessing step, improving translation accuracy through better grammatical understanding while managing complexity through modular architecture.

The patent introduces syntactic parse trees as an intermediary representation between the input sentence and the translation output. These parse trees serve as a structured mediator that captures grammatical relationships, enabling the translation model to leverage syntactic information without directly embedding complex syntax analysis within the core translation architecture, thus balancing accuracy improvement with complexity management.

The patent merges dependency parsing with constituency parsing by integrating the strengths of both approaches. The model uses dependency relations to capture word-to-word relationships while incorporating constituency structure to represent hierarchical phrase relationships. This combination allows the system to maintain implementation ease through dependency parsing while improving translation accuracy for complex sentences through constituency information.

The patent creates a composite syntactic representation that combines multiple parsing methodologies. By integrating dependency parse trees with constituency structure and grammatical role annotations, the model forms a composite syntactic framework that leverages the implementation simplicity of dependency parsing while enhancing translation accuracy through additional structural information from constituency analysis.