@inproceedings{fusco-etal-2025-linguistic,
    title = "Linguistic Units as Tokens: Intrinsic and Extrinsic Evaluation with {B}aby{LM}",
    author = "Fusco, Achille  and
      Bianchessi, Maria Letizia Piccini  and
      Sgrizzi, Tommaso  and
      Zanollo, Asya  and
      Chesi, Cristiano",
    editor = "Charpentier, Lucas  and
      Choshen, Leshem  and
      Cotterell, Ryan  and
      Gul, Mustafa Omer  and
      Hu, Michael Y.  and
      Liu, Jing  and
      Jumelet, Jaap  and
      Linzen, Tal  and
      Mueller, Aaron  and
      Ross, Candace  and
      Shah, Raj Sanjay  and
      Warstadt, Alex  and
      Wilcox, Ethan Gotlieb  and
      Williams, Adina",
    booktitle = "Proceedings of the First BabyLM Workshop",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.babylm-main.35/",
    doi = "10.18653/v1/2025.babylm-main.35",
    pages = "496--507",
    ISBN = "TODO",
    abstract = "Tokenization is often treated as a preprocessing step, yet in data-limited settings it directly shapes what a model can learn. We compare four segmentation strategies in the BabyLM Challenge: frequency-based BPE, morphology-aware MorPiece and ParadigmFinder, and syllable-based SylliTok. Evaluation combines two perspectives. First, an intrinsic test on the SIGMORPHON 2022 segmentation benchmark, adapted to English, measures how closely each tokenizer aligns with morpheme boundaries. Second, extrinsic tests train GPT-2 on the 10M BabyLM corpus and evaluate on the 2025 benchmark. No single tokenizer dominates. BPE remains strong on syntax-heavy tasks. ParadigmFinder excels in semantic composition and age-of-acquisition alignment. MorPiece shows advantages in discourse tracking. Morphology-aware tokenizers achieve the best intrinsic segmentation scores, and these gains translate into more robust generalisation in comprehension tasks. These results highlight tokenization as a core modeling decision, with direct consequences for compression, morphology, and the path to humanlike learning."
}