Grammar System of TCFL Driven by Neural Network Technology

Area of Science:

• Computational Linguistics
• Educational Technology
• Second Language Acquisition

Background:

• Increasing global interest in Chinese language and culture necessitates advancements in Teaching Chinese as a Foreign Language (TCFL).
• Information technology and curriculum integration are transforming TCFL methodologies.
• Existing TCFL grammar research requires a fresh perspective in light of these changes.

Purpose of the Study:

• To propose and validate a novel grammar error correction scheme for TCFL.
• To enhance the accuracy and efficiency of correcting Chinese grammar errors in foreign language learners.
• To investigate the effectiveness of integrating cultural vocabulary into TCFL.

Main Methods:

• Development of a hybrid grammar error correction model combining Transformer and N-gram models.
• Dynamic combination of outputs from different neural modules to improve semantic information capture.
• Experimental validation through specific teaching practice and analysis of error correction performance.

Main Results:

• The hybrid Transformer and N-gram model demonstrates strong performance in global error correction effects.
• The model achieves the best results in error detection and positioning.
• At the detection level, the model achieved a 0.64 accuracy and 0.67 recall rate.
• Incorporating an attention mechanism improved the grammatical error correction model's computational efficiency.

Conclusions:

• The proposed hybrid model offers a superior approach to Chinese grammar error correction in TCFL.
• The strategy effectively enhances the detection and correction of grammatical errors for learners.
• Attention mechanisms are beneficial for improving the efficiency of grammatical error correction models.