Parallel Tokenizers: Rethinking Vocabulary Design for Cross-Lingual Transfer

• URL: http://arxiv.org/abs/2510.06128v1
• Date: Tue, 07 Oct 2025 17:05:49 GMT
• Title: Parallel Tokenizers: Rethinking Vocabulary Design for Cross-Lingual Transfer
• Authors: Muhammad Dehan Al Kautsar, Fajri Koto,
• Abstract summary: Tokenization defines the foundation of multilingual language models.<n>New framework trains tokenizers monolingually and aligns vocabularies exhaustively using bilingual dictionaries or word-to-word translation.
• Score: 13.630754537249707
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Tokenization defines the foundation of multilingual language models by determining how words are represented and shared across languages. However, existing methods often fail to support effective cross-lingual transfer because semantically equivalent words are assigned distinct embeddings. For example, "I eat rice" in English and "Ina cin shinkafa" in Hausa are typically mapped to different vocabulary indices, preventing shared representations and limiting cross-lingual generalization. We introduce parallel tokenizers. This new framework trains tokenizers monolingually and then aligns their vocabularies exhaustively using bilingual dictionaries or word-to-word translation, ensuring consistent indices for semantically equivalent words. This alignment enforces a shared semantic space across languages while naturally improving fertility balance. To assess their effectiveness, we pretrain a transformer encoder from scratch on thirteen low-resource languages and evaluate it on sentiment analysis, hate speech detection, emotion classification, and sentence embedding similarity. Across all tasks, models trained with parallel tokenizers outperform conventional multilingual baselines, confirming that rethinking tokenization is essential for advancing multilingual representation learning--especially in low-resource settings.

Related papers

• False Friends Are Not Foes: Investigating Vocabulary Overlap in Multilingual Language Models [53.01170039144264]
  Subword tokenizers trained on multilingual corpora naturally produce overlapping tokens across languages.<n>Does token overlap facilitate cross-lingual transfer or instead introduce interference between languages?<n>We find that models with overlap outperform models with disjoint vocabularies.
  arXiv  Detail & Related papers  (2025-09-23T07:47:54Z)
• Tokens with Meaning: A Hybrid Tokenization Approach for NLP [0.2826977330147589]
  Tokenization plays a pivotal role in natural language processing (NLP)<n>We introduce a hybrid tokenization framework that combines rule-based morphological analysis with statistical subword segmentation.<n>The method uses phono normalization, root-affix, and a novel algorithm that balances morpheme preservation with vocabulary efficiency.
  arXiv  Detail & Related papers  (2025-08-19T22:17:42Z)
• Beyond Literal Token Overlap: Token Alignability for Multilinguality [53.680462160878925]
  We propose subword token alignability as a new way to understand the impact and quality of multilingual tokenisation.<n>In particular, this metric predicts multilinguality much better when scripts are disparate and the overlap of literal tokens is low.<n>We recommend our subword token alignability metric for identifying optimal language pairs for cross-lingual transfer.
  arXiv  Detail & Related papers  (2025-02-10T13:50:12Z)
• Tomato, Tomahto, Tomate: Measuring the Role of Shared Semantics among Subwords in Multilingual Language Models [88.07940818022468]
  We take an initial step on measuring the role of shared semantics among subwords in the encoder-only multilingual language models (mLMs) We form "semantic tokens" by merging the semantically similar subwords and their embeddings. inspections on the grouped subwords show that they exhibit a wide range of semantic similarities.
  arXiv  Detail & Related papers  (2024-11-07T08:38:32Z)
• Beyond Shared Vocabulary: Increasing Representational Word Similarities across Languages for Multilingual Machine Translation [9.794506112999823]
  In this paper, we define word-level information transfer pathways via word equivalence classes and rely on graph networks to fuse word embeddings across languages. Our experiments demonstrate the advantages of our approach: 1) embeddings of words with similar meanings are better aligned across languages, 2) our method achieves consistent BLEU improvements of up to 2.3 points for high- and low-resource MNMT, and 3) less than 1.0% additional trainable parameters are required with a limited increase in computational costs.
  arXiv  Detail & Related papers  (2023-05-23T16:11:00Z)
• Learning Contextualised Cross-lingual Word Embeddings and Alignments for Extremely Low-Resource Languages Using Parallel Corpora [63.5286019659504]
  We propose a new approach for learning contextualised cross-lingual word embeddings based on a small parallel corpus. Our method obtains word embeddings via an LSTM encoder-decoder model that simultaneously translates and reconstructs an input sentence.
  arXiv  Detail & Related papers  (2020-10-27T22:24:01Z)
• On the Language Neutrality of Pre-trained Multilingual Representations [70.93503607755055]
  We investigate the language-neutrality of multilingual contextual embeddings directly and with respect to lexical semantics. Our results show that contextual embeddings are more language-neutral and, in general, more informative than aligned static word-type embeddings. We show how to reach state-of-the-art accuracy on language identification and match the performance of statistical methods for word alignment of parallel sentences.
  arXiv  Detail & Related papers  (2020-04-09T19:50:32Z)
• 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.