Scripts Through Time: A Survey of the Evolving Role of Transliteration in NLP

2026-04-20 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, quick

Summary

A new survey examines the evolving role of transliteration in Natural Language Processing (NLP), particularly its application in cross-lingual transfer. The paper introduces a taxonomy of motivations for using transliteration in language models and details various approaches for incorporating transliterated input. It analyzes the effectiveness and evolution of these methods, discussing critical trade-offs and their relevance for modern Large Language Models (LLMs). The review highlights transliteration's benefits in diverse scenarios, such as processing code-mixed text, exploiting language family relatedness, and achieving pragmatic gains in inference efficiency. This analysis culminates in specific recommendations for researchers on selecting and implementing optimal transliteration strategies based on language, task, and resource limitations.

Key takeaway

For NLP researchers and engineers working on cross-lingual applications, understanding transliteration's role is crucial. You should evaluate different transliteration strategies based on your specific language, task, and resource constraints to overcome script barriers and improve model performance. Consider its benefits for code-mixing, language relatedness, and inference efficiency in your LLM deployments.

Key insights

Transliteration bridges script barriers in cross-lingual NLP, enhancing lexical overlap and transfer learning.

Principles

• Script differences inhibit cross-lingual transfer.
• Transliteration increases lexical overlap.
• Contextualize transliteration for modern LLMs.

Method

The survey provides a taxonomy of motivations for using transliterations and overviews different approaches for incorporating them as input into language models, analyzing their evolution and effectiveness.

In practice

• Handle code-mixed text effectively.
• Leverage language family relatedness.
• Improve inference efficiency.

Topics

• Transliteration
• Cross-lingual NLP
• Script Barrier
• Large Language Models
• Code-mixed Text

Best for: AI Engineer, Machine Learning Engineer, Research Scientist, AI Scientist, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.