@inproceedings{teklehaymanot-etal-2025-movoc,
    title = "{M}o{V}o{C}: Morphology-Aware Subword Construction for {G}e{'}ez Script Languages",
    author = "Teklehaymanot, Hailay Kidu  and
      Fazlija, Dren  and
      Nejdl, Wolfgang",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-luhme/2025.findings-emnlp.706/",
    doi = "10.18653/v1/2025.findings-emnlp.706",
    pages = "13131--13144",
    ISBN = "979-8-89176-335-7",
    abstract = "Subword-based tokenization methods often fail to preserve morphological boundaries, a limitation especially pronounced in low-resource, morphologically complex languages such as those written in the Ge{`}ez script. To address this, we present MoVoC (Morpheme-aware Subword Vocabulary Construction) and train MoVoC-Tok, a tokenizer that integrates supervised morphological analysis into the subword vocabulary. This hybrid segmentation approach combines morpheme-based and Byte Pair Encoding (BPE) tokens to preserve morphological integrity while maintaining lexical meaning. To tackle resource scarcity, we curate and release manually annotated morpheme data for four Ge{`}ez script languages and a morpheme-aware vocabulary for two of them. While the proposed tokenization method does not lead to significant gains in automatic translation quality, we observe consistent improvements in intrinsic metrics, MorphoScore, and Boundary Precision, highlighting the value of morphology-aware segmentation in enhancing linguistic fidelity and token efficiency. Our morpheme-annotated datasets and tokenizer dataset will be publicly available under the Open Data licenses to support further research in low-resource, morphologically rich languages."
}