Universal or Language-Family-Specific Script Unification for Cross-Lingual Transfer? A Case Study on Turkic Languages

• URL: http://arxiv.org/abs/2608.09356v1
• Date: Mon, 10 Aug 2026 09:35:38 GMT
• Title: Universal or Language-Family-Specific Script Unification for Cross-Lingual Transfer? A Case Study on Turkic Languages
• Abstract summary: We compare two approaches to script unification: the general-purpose uroman romanizer and the family-specific Common Turkic Script (CTS)<n>We train matched fastText models on transliterated Wikipedia corpora from 11 Turkic languages and evaluate them on WikiANN named entity recognition and Universal Dependencies part-of-speech tagging.
• Score: 1.2708506244327207
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Closely related languages written in different scripts expose little surface overlap to multilingual models, limiting cross-lingual transfer. We compare two approaches to script unification: the general-purpose uroman romanizer and the family-specific Common Turkic Script (CTS). We train matched fastText models on transliterated Wikipedia corpora from 11 Turkic languages and evaluate them on WikiANN named entity recognition and Universal Dependencies part-of-speech tagging. CTS and uroman show no significant difference on NER, while both substantially outperform the official monolingual fastText baselines. POS results reveal no universal winner: language-specific differences are associated with the cross-lingual character n-gram coverage induced by each representation, while within-language coverage becomes more important when target-language supervision is available. Although CANINE-c achieves higher overall POS averages, the substantially simpler fastText-based systems remain competitive on several treebanks. Overall, the effectiveness of script unification depends on the language, the induced subword overlap, and the available supervision.

Related papers

• Tokenizing Crosslingual Homographs [8.254230288283258]
  Multilingual language models rely on shared subword vocabularies to represent multiple languages within a limited number of token units.<n>We investigate this limitation through cross-lingual homographs and false friends.<n>We propose a simple tokenizer-level intervention based on language cues: language-specific characters replacing initial characters of shared-vocabulary words.
  arXiv  Detail & Related papers  (2026-07-20T08:37:31Z)
• A two-stage transliteration approach to improve performance of a multilingual ASR [1.9511556030544333]
  This paper presents an approach to build a language-agnostic end-to-end model trained on a grapheme set. We performed experiments with an end-to-end multilingual speech recognition system for two Indic languages.
  arXiv  Detail & Related papers  (2024-10-09T05:30:33Z)
• Breaking the Script Barrier in Multilingual Pre-Trained Language Models with Transliteration-Based Post-Training Alignment [50.27950279695363]
  The transfer performance is often hindered when a low-resource target language is written in a different script than the high-resource source language. Inspired by recent work that uses transliteration to address this problem, our paper proposes a transliteration-based post-pretraining alignment (PPA) method.
  arXiv  Detail & Related papers  (2024-06-28T08:59:24Z)
• MYTE: Morphology-Driven Byte Encoding for Better and Fairer Multilingual Language Modeling [70.34758460372629]
  We introduce a new paradigm that encodes the same information with segments of consistent size across diverse languages. MYTE produces shorter encodings for all 99 analyzed languages. This, in turn, improves multilingual LM performance and diminishes the perplexity gap throughout diverse languages.
  arXiv  Detail & Related papers  (2024-03-15T21:21:11Z)
• 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)
• Discovering Representation Sprachbund For Multilingual Pre-Training [139.05668687865688]
  We generate language representation from multilingual pre-trained models and conduct linguistic analysis. We cluster all the target languages into multiple groups and name each group as a representation sprachbund. Experiments are conducted on cross-lingual benchmarks and significant improvements are achieved compared to strong baselines.
  arXiv  Detail & Related papers  (2021-09-01T09:32:06Z)
• Syntax-augmented Multilingual BERT for Cross-lingual Transfer [37.99210035238424]
  This work shows that explicitly providing language syntax and training mBERT helps cross-lingual transfer. Experiment results show that syntax-augmented mBERT improves cross-lingual transfer on popular benchmarks.
  arXiv  Detail & Related papers  (2021-06-03T21:12:50Z)
• VECO: Variable and Flexible Cross-lingual Pre-training for Language Understanding and Generation [77.82373082024934]
  We plug a cross-attention module into the Transformer encoder to explicitly build the interdependence between languages. It can effectively avoid the degeneration of predicting masked words only conditioned on the context in its own language. The proposed cross-lingual model delivers new state-of-the-art results on various cross-lingual understanding tasks of the XTREME benchmark.
  arXiv  Detail & Related papers  (2020-10-30T03:41:38Z)
• FILTER: An Enhanced Fusion Method for Cross-lingual Language Understanding [85.29270319872597]
  We propose an enhanced fusion method that takes cross-lingual data as input for XLM finetuning. During inference, the model makes predictions based on the text input in the target language and its translation in the source language. To tackle this issue, we propose an additional KL-divergence self-teaching loss for model training, based on auto-generated soft pseudo-labels for translated text in the target language.
  arXiv  Detail & Related papers  (2020-09-10T22:42:15Z)

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.