The application relates to a Vietnamese dependency
syntax analysis method and
system based on multiple annotators, and belongs to the field of
natural language processing. The application fine-tunes the parameters of an XLM RoBERTa model using unlabeled data. Then, an initial Vietnamese dependency
syntax analysis model is trained using a UD tree
library, a
sentence is input into the model for analysis, and
pseudo data containing
noise is generated. The
pseudo data is input into a pre-designed prompt template, a large
language model DeepSeek is used for secondary
annotation,
noise data is gradually corrected through prompt learning, and high-quality
annotation results are output. Finally, the
pseudo data after secondary
annotation is used as additional training corpus, combined with original annotation data, and a new Vietnamese
syntax analysis model is trained. The application introduces a pseudo data enhancement and multiple annotator collaborative optimization mechanism, and significantly improves the performance of the model in the Vietnamese dependency syntax analysis task.