Semi-supervised learning and bidirectional decoding for effective grammar correction in low-resource scenarios.

Area of Science:

• Natural Language Processing
• Computational Linguistics

Background:

• Grammatical Error Correction (GEC) is vital for language accuracy.
• Low-resource languages lack sufficient training data for effective GEC.
• Classical seq2seq GEC models suffer from unbalanced outputs and exposure bias.

Purpose of the Study:

• Propose a novel GEC framework for low-resource languages.
• Address data scarcity through a semi-supervised synthetic data generation method.
• Overcome limitations of unidirectional decoders and exposure bias in seq2seq models.

Main Methods:

• Developed the Equal Distribution of Synthetic Errors (EDSE) method for data augmentation.
• Applied knowledge distillation from neural machine translation with dual decoders (forward and backward).
• Utilized Kullback-Leibler divergence for decoder agreement regularization.

Main Results:

• The proposed framework outperformed Transformer baseline and bidirectional decoding techniques.
• Achieved the highest F1 score on two benchmarks.
• EDSE significantly improved performance, especially for syntactic errors.

Conclusions:

• The novel GEC framework is effective for low-resource languages, demonstrated by Arabic.
• Data augmentation and bidirectional decoding strategies enhance GEC accuracy.
• The approach offers a viable solution for improving written language quality in data-scarce scenarios.