How to Progressively Build Thai Spelling Correction Systems?

Lertpiya, A., Chalothorn, T., Buabthong, P., "How to Progressively Build Thai Spelling Correction Systems?." IEEE Access, (2023). DOI: 10.1109/ACCESS.2023.3295004

tl;dr

• The paper discusses the challenges of building Thai spelling correction systems due to the language's complexity.
• It proposes a method to progressively build correction systems using minimal annotated data.
• Introduces the Extendable Neural Contextual Corrector (XNCC), which allows vocabulary updates without retraining.
• Highlights the limitations of traditional dictionary-based and Seq2Seq models in handling out-of-vocabulary (OOV) words.
• Recommends a hybrid approach combining neural-based models and dictionary methods for better performance.
• The data annotation process is optimized to reduce human effort while maintaining accuracy.
• Experimental results show that significant improvements can be made with minor changes to existing methods.

Comments

• Incorporating more extensive datasets to include a broader variety of text sources, particularly for handling diverse linguistic patterns and rare words.
• Enhancing the model's ability to understand broader context in sentences by integrating more advanced language models that capture nuanced meanings.
• Further refining the balance between neural-based models and dictionary-based methods to optimize performance, particularly in cases with out-of-vocabulary words.
• Implementing a feedback loop where user corrections are continuously incorporated into the model, allowing it to learn from real-world usage and improve over time.