A Grapheme-Aware Indic Tokenizer for Tamil: Large-Scale Training and Intrinsic Evaluation

Abstract:Tokenization forms the foundation of modern Natural Language Processing (NLP) systems by transforming raw text into discrete units that neural language models can process. The effectiveness of this process directly influences vocabulary efficiency, sequence length, computational cost, and downstream model performance. Although multilingual tokenizers such as Byte Pair Encoding (BPE), WordPiece, and SentencePiece have performed well across numerous languages, they often segment morphologically rich Indic languages inefficiently. Tamil, in particular, poses unique challenges because its grapheme-based writing system can represent a single visible character with multiple Unicode code points. In this work, we present a grapheme-aware Indic tokenizer for Tamil that preserves complete grapheme clusters through a reversible Unicode mapping strategy prior to WordPiece vocabulary learning. By operating on grapheme-level representations instead of individual Unicode code points, the tokenizer produces linguistically meaningful token boundaries while remaining fully compatible with transformer-based language models. The tokenizer is trained on a large-scale Tamil corpus and evaluated using a comprehensive intrinsic evaluation framework that measures compression efficiency, token fragmentation, information density, and vocabulary utilization. Experimental evaluation compares the proposed tokenizer against five widely used multilingual tokenizers: GPT-2, mBERT, mT5, mBART, and NLLB. The proposed tokenizer achieves the strongest performance among the evaluated tokenizers on fragmentation- and sequence-efficiency-oriented intrinsic metrics, while matching the highest observed compression ratio. These results demonstrate the effectiveness of grapheme-aware preprocessing for Tamil tokenization.

Computation and Language (cs.CL)

arXiv:2609.06690 [cs.CL]

(or arXiv:2609.06690v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.06690 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.