Scripts Through Time: A Survey of the Evolving Role of Transliteration in NLP
- URL: http://arxiv.org/abs/2604.18722v1
- Date: Mon, 20 Apr 2026 18:20:35 GMT
- Title: Scripts Through Time: A Survey of the Evolving Role of Transliteration in NLP
- Abstract summary: Cross-lingual transfer in NLP is often hindered by the script barrier'' where differences in writing systems inhibit transfer learning between languages.<n>Transliteration, the process of converting the script, has emerged as a powerful technique to bridge this gap by increasing lexical overlap.
- Score: 16.32377980108084
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Cross-lingual transfer in NLP is often hindered by the ``script barrier'' where differences in writing systems inhibit transfer learning between languages. Transliteration, the process of converting the script, has emerged as a powerful technique to bridge this gap by increasing lexical overlap. This paper provides a comprehensive survey of the application of transliteration in cross-lingual NLP. We present a taxonomy of key motivations to utilize transliterations in language models, and provide an overview of different approaches of incorporating transliterations as input. We analyze the evolution and effectiveness of these methods, discussing the critical trade-offs involved, and contextualize their need in modern LLMs. The review explores various settings that show how transliteration is beneficial, including handling code-mixed text, leveraging language family relatedness, and pragmatic gains in inference efficiency. Based on this analysis, we provide concrete recommendations for researchers on selecting and implementing the most appropriate transliteration strategy based on their specific language, task, and resource constraints.
Related papers
- When Meanings Meet: Investigating the Emergence and Quality of Shared Concept Spaces during Multilingual Language Model Training [57.230355403478995]
We investigate the development of language-agnostic concept spaces during pretraining of EuroLLM.<n>We find that shared concept spaces emerge early and continue to refine, but that alignment with them is language-dependent.<n>In contrast to prior work, our fine-grained manual analysis reveals that some apparent gains in translation quality reflect shifts in behavior.
arXiv Detail & Related papers (2026-01-30T11:23:01Z) - Predicate-Argument Structure Divergences in Chinese and English Parallel Sentences and their Impact on Language Transfer [6.834698677197089]
Cross-lingual Natural Language Processing offers practical solutions in low-resource settings.<n> linguistic divergences hinder language transfer, especially among typologically distant languages.<n>We present an analysis of predicate-argument structures in parallel Chinese and English sentences.
arXiv Detail & Related papers (2025-11-12T22:55:29Z) - Enhancing Entertainment Translation for Indian Languages using Adaptive Context, Style and LLMs [3.55026004901472]
We introduce an algorithm to estimate the context and style of the current session and use these estimations to generate a prompt that guides a Large Language Model (LLM) to generate high-quality translations.<n>Our method is both language and LLM-agnostic, making it a general-purpose tool.
arXiv Detail & Related papers (2024-12-29T11:33:51Z) - LLM-based Translation Inference with Iterative Bilingual Understanding [52.46978502902928]
We propose a novel Iterative Bilingual Understanding Translation method based on the cross-lingual capabilities of large language models (LLMs)<n>The cross-lingual capability of LLMs enables the generation of contextual understanding for both the source and target languages separately.<n>The proposed IBUT outperforms several strong comparison methods.
arXiv Detail & Related papers (2024-10-16T13:21:46Z) - Trans-Tokenization and Cross-lingual Vocabulary Transfers: Language Adaptation of LLMs for Low-Resource NLP [13.662528492286528]
We present a novel cross-lingual vocabulary transfer strategy, trans-tokenization, designed to tackle this challenge and enable more efficient language adaptation.
Our approach focuses on adapting a high-resource monolingual LLM to an unseen target language by initializing the token embeddings of the target language using a weighted average of semantically similar token embeddings from the source language.
We introduce Hydra LLMs, models with multiple swappable language modeling heads and embedding tables, which further extend the capabilities of our trans-tokenization strategy.
arXiv Detail & Related papers (2024-08-08T08:37:28Z) - (Perhaps) Beyond Human Translation: Harnessing Multi-Agent Collaboration for Translating Ultra-Long Literary Texts [56.7988577327046]
We introduce TransAgents, a novel multi-agent framework that simulates the roles and collaborative practices of a human translation company.<n>Our findings highlight the potential of multi-agent collaboration in enhancing translation quality, particularly for longer texts.
arXiv Detail & Related papers (2024-05-20T05:55:08Z) - Decomposed Prompting: Probing Multilingual Linguistic Structure Knowledge in Large Language Models [54.58989938395976]
We introduce a decomposed prompting approach for sequence labeling tasks.<n>We test our method on the Universal Dependencies part-of-speech tagging dataset for 38 languages.
arXiv Detail & Related papers (2024-02-28T15:15:39Z) - Multilingual Lexical Simplification via Paraphrase Generation [19.275642346073557]
We propose a novel multilingual LS method via paraphrase generation.
We regard paraphrasing as a zero-shot translation task within multilingual neural machine translation.
Our approach surpasses BERT-based methods and zero-shot GPT3-based method significantly on English, Spanish, and Portuguese.
arXiv Detail & Related papers (2023-07-28T03:47:44Z) - Enhancing Cross-lingual Transfer via Phonemic Transcription Integration [57.109031654219294]
PhoneXL is a framework incorporating phonemic transcriptions as an additional linguistic modality for cross-lingual transfer.
Our pilot study reveals phonemic transcription provides essential information beyond the orthography to enhance cross-lingual transfer.
arXiv Detail & Related papers (2023-07-10T06:17:33Z) - Revamping Multilingual Agreement Bidirectionally via Switched
Back-translation for Multilingual Neural Machine Translation [107.83158521848372]
multilingual agreement (MA) has shown its importance for multilingual neural machine translation (MNMT)
We present textbfBidirectional textbfMultilingual textbfAgreement via textbfSwitched textbfBack-textbftranslation (textbfBMA-SBT)
It is a novel and universal multilingual agreement framework for fine-tuning pre-trained MNMT models.
arXiv Detail & Related papers (2022-09-28T09:14:58Z) - Exposing Cross-Lingual Lexical Knowledge from Multilingual Sentence
Encoders [85.80950708769923]
We probe multilingual language models for the amount of cross-lingual lexical knowledge stored in their parameters, and compare them against the original multilingual LMs.
We also devise a novel method to expose this knowledge by additionally fine-tuning multilingual models.
We report substantial gains on standard benchmarks.
arXiv Detail & Related papers (2022-04-30T13:23:16Z) - Robust Cross-lingual Embeddings from Parallel Sentences [65.85468628136927]
We propose a bilingual extension of the CBOW method which leverages sentence-aligned corpora to obtain robust cross-lingual word representations.
Our approach significantly improves crosslingual sentence retrieval performance over all other approaches.
It also achieves parity with a deep RNN method on a zero-shot cross-lingual document classification task.
arXiv Detail & Related papers (2019-12-28T16:18:33Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.