Multilingual Tokenization through the Lens of Indian Languages: Challenges and Insights

• URL: http://arxiv.org/abs/2506.17789v2
• Date: Tue, 24 Jun 2025 09:35:36 GMT
• Title: Multilingual Tokenization through the Lens of Indian Languages: Challenges and Insights
• Authors: N J Karthika, Maharaj Brahma, Rohit Saluja, Ganesh Ramakrishnan, Maunendra Sankar Desarkar,
• Abstract summary: This paper presents an intrinsic evaluation of tokenization strategies across 17 Indian languages.<n>We quantify the trade-offs between bottom-up and top-down tokenizer algorithms.<n>We show that extremely low-resource languages can benefit from tokenizers trained on related high-resource languages.
• Score: 27.369278566345074
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tokenization plays a pivotal role in multilingual NLP. However, existing tokenizers are often skewed towards high-resource languages, limiting their effectiveness for linguistically diverse and morphologically rich languages such as those in the Indian subcontinent. This paper presents a comprehensive intrinsic evaluation of tokenization strategies across 17 Indian languages. We quantify the trade-offs between bottom-up and top-down tokenizer algorithms (BPE and Unigram LM), effects of vocabulary sizes, and compare strategies of multilingual vocabulary construction such as joint and cluster-based training. We also show that extremely low-resource languages can benefit from tokenizers trained on related high-resource languages. Our study provides practical insights for building more fair, efficient, and linguistically informed tokenizers for multilingual NLP.

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