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

Multilingual tokenization is foundational for NLP, especially with the rise of large pre-trained models. While high-resource languages like English benefit from abundant data and mature algorithms, Indic languages pose unique challenges due to their complex morphology, diverse scripts, and resource scarcity. Prior works such as BPE (Sennrich et al., 2016) and Unigram LM (Kudo, 2018) have shown promise but lack systematic evaluation across multiple Indic languages. Recent efforts focus on script normalization and vocabulary sharing, yet comprehensive large-scale comparisons remain scarce. Addressing these gaps is crucial for improving NLP tools for India’s linguistic diversity, enabling better machine translation, information retrieval, and speech recognition for low-resource languages.

The core challenge lies in designing tokenization strategies that effectively capture morphological and script-specific features across diverse Indic languages. Existing models tend to favor high-resource languages, leading to inefficient vocabulary use and poor morphological boundary preservation in low-resource languages. Additionally, the impact of script normalization and vocabulary construction strategies on downstream NLP tasks remains underexplored. Balancing multilingual transfer with language-specific nuances is a key bottleneck, requiring methods that adapt to morphological richness and script diversity without inflating vocabulary size or computational costs.

This work introduces a multi-faceted approach combining script normalization, subword algorithm comparison, and clustering-based vocabulary construction. Script normalization reduces orthographic variability, enhancing token consistency. The comparative analysis of BPE and ULM reveals that ULM better preserves morphological boundaries, especially when combined with normalization. Clustering languages based on linguistic similarity improves vocabulary efficiency and downstream task performance, especially for low-resource languages. The systematic evaluation across 17 languages and multiple vocabulary sizes provides new insights into optimizing multilingual tokenization for morphologically complex scripts.

• �� Collect high-quality corpora for 17 Indic languages, apply IndicNLP normalization to standardize scripts. • Train BPE and ULM tokenizers at vocab sizes of 32K, 64K, 128K, 256K. • Implement joint vocabulary training by concatenating all corpora; develop clustering-based strategies by grouping similar languages via K-means on language vectors. • Evaluate tokenizers intrinsically using fertility, Character Per Token, MorphScore; extrinsically via transformer-based models on NLI and NER tasks. • Compare normalized versus non-normalized corpora, analyze vocabulary overlap, and measure downstream task accuracy to determine optimal strategies.

Experiments involve training tokenizers on normalized and non-normalized corpora across all languages, then evaluating intrinsic metrics such as fertility and MorphScore. Downstream performance is tested by pretraining transformer models on the tokenized data, fine-tuning on NLI and NER datasets. The hyperparameters include vocab sizes, learning rates, and clustering configurations. Results are validated through cross-validation and statistical significance testing, ensuring robustness. The study systematically compares joint and cluster-based vocabularies, analyzing their impact on morphological preservation and task accuracy.

Normalized corpora consistently outperform non-normalized data, reducing fertility from 1.717 to 1.664 at 128k vocab, and increasing MorphScore by 3-5%. Clustering strategies improve low-resource language performance, with the best clusters based on linguistic similarity. Vocabulary overlap peaks at 128k (~70%) and declines at 256k, indicating diminishing returns. Downstream tasks show that models trained with clustered vocabularies outperform joint models, especially in morphologically rich languages, confirming the effectiveness of linguistically informed strategies.

The proposed methods can be directly applied to improve multilingual NLP systems, including machine translation, speech recognition, and cross-lingual information retrieval, especially for low-resource Indian languages. They enable more efficient vocabulary usage, better morphological understanding, and improved transfer learning. Long-term, these strategies can facilitate the development of inclusive AI models that understand and generate multiple languages with minimal resource requirements, impacting education, communication, and technology accessibility.

Current datasets may not fully represent dialectal and colloquial variations, limiting generalizability. The computational cost of training multiple tokenizers and models is high, posing challenges for real-time deployment. Support for extremely low-resource or dialectal languages remains limited, requiring further data collection and model adaptation. Additionally, morphological datasets are sparse for some languages, affecting the accuracy of evaluation metrics like MorphScore.