@inproceedings{bauwens-de-lhoneux-2026-rebpe,
    title = "{R}e{BPE}: Iteratively Improving the Internal Structure of a Structured Tokeniser by Mining its Internal Structure",
    author = "Bauwens, Thomas  and
      de Lhoneux, Miryam",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.findings-eacl.211/",
    pages = "4075--4090",
    ISBN = "979-8-89176-386-9",
    abstract = "Recent work has explored pruning merges from BPE subword tokenisers using corpus data as a signal for which merges to prune. We argue that because a BPE tokeniser contains a rich data structure on top of its vocabulary set, this in itself can be used as a guide to modify its merges such that segmentations become more desirable. We apply this argument to one of those pruning algorithms, BPE-knockout, by introducing a new reification step that suggests new merges by inspecting the effects left by pruning. By alternating both processes iteratively until convergence, we get a new BPE tokeniser, ReBPE, which outperforms the original BPE-knockout algorithm on morphological alignment in all 14 languages tested by over 11{\%} F1 on average."
}