TamilMayangoliSpell: An Open-Source Neural Framework for Context-Sensitive Mayangoli Error Correction in Tamil

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Advanced, medium

Summary

TamilMayangoliSpell is an open-source neural framework designed to correct context-sensitive Mayangoli errors in Tamil, which arise from confusion among phonetically similar graphemes like ல/ள/ழ. These errors pose a challenge for traditional spell checkers because both incorrect and correct forms are valid dictionary words, requiring contextual understanding. The framework integrates Tamil-specific preprocessing for sentence segmentation and normalization, linguistically grounded error induction to generate dictionary-valid training data, and fine-tuning of multilingual sequence-to-sequence models. Utilizing 30,000 sentence pairs from the TamilCorp corpus, the researchers fine-tuned mBART, mT5, and NLLB. mT5 demonstrated the best performance, achieving a BLEU score of 99.28 and an Exact Match Accuracy of 93.50%, maintaining strong results in cross-genre evaluations on short stories. All associated scripts, datasets, and trained models are publicly available on GitHub.

Key takeaway

For NLP Engineers or AI Scientists developing language tools for Dravidian languages, this framework offers a robust solution for context-sensitive spelling correction. You should consider fine-tuning mT5 for similar challenges, especially where phonetically similar graphemes cause errors that dictionary lookups cannot resolve. The publicly available resources provide a strong baseline to accelerate your development of high-accuracy, context-aware spell checkers.

Key insights

Context-sensitive Mayangoli errors in Tamil require neural models due to dictionary validity of both correct and incorrect forms.

Principles

• Contextual modeling is crucial for homophone errors.
• Linguistically-grounded data generation improves training.
• Multilingual models can be fine-tuned for specific languages.

Method

The framework involves Tamil-specific preprocessing, linguistically grounded error induction for training data generation, and fine-tuning multilingual sequence-to-sequence models (mBART, mT5, NLLB) on 30,000 sentence pairs.

In practice

• Use mT5 for high-accuracy Tamil spell correction.
• Leverage TamilCorp for corpus-based language tasks.
• Adapt error induction for similar language challenges.

Topics

• Tamil NLP
• Mayangoli Error Correction
• Sequence-to-Sequence Models
• mT5
• Multilingual Models
• Natural Language Processing
• Dravidian Languages

Best for: Research Scientist, AI Scientist, NLP Engineer, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.