Hierarchical Transformer Encoders for Vietnamese Spelling Correction
This work addresses spelling correction for Vietnamese, a domain-specific problem, with incremental improvements over existing methods.
The authors tackled Vietnamese spelling correction by proposing a Hierarchical Transformer model that uses character-level and word-level information to detect and correct errors, achieving state-of-the-art performance with improved recall, precision, and f1-score metrics.
In this paper, we propose a Hierarchical Transformer model for Vietnamese spelling correction problem. The model consists of multiple Transformer encoders and utilizes both character-level and word-level to detect errors and make corrections. In addition, to facilitate future work in Vietnamese spelling correction tasks, we propose a realistic dataset collected from real-life texts for the problem. We compare our method with other methods and publicly available systems. The proposed method outperforms all of the contemporary methods in terms of recall, precision, and f1-score. A demo version is publicly available.