Papers › Splintering Nonconcatenative Languages for Better Tokenization

Splintering Nonconcatenative Languages for Better Tokenization

18 Mar 2025arXiv:2503.14433archive 2025-07-28

Bar Gazit, Shaltiel Shmidman, Avi Shmidman, Yuval Pinter

Common subword tokenization algorithms like BPE and UnigramLM assume that text can be split into meaningful units by concatenative measures alone. This is not true for languages such as Hebrew and Arabic, where morphology is encoded in root-template patterns, or Malay and Georgian, where split affixes are common. We present SPLINTER, a pre-processing step which rearranges text into a linear form that better represents such nonconcatenative morphologies, enabling meaningful contiguous segments to be found by the tokenizer. We demonstrate SPLINTER's merit using both intrinsic measures evaluating token vocabularies in Hebrew, Arabic, and Malay; as well as on downstream tasks using BERT-architecture models trained for Hebrew.

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