@inproceedings{nonomura-etal-2026-disentangling,
    title = "Disentangling Meaning and Language Components in Diverse Multilingual Sentence Embeddings",
    author = "Nonomura, Kanade  and
      Fukushima, Keita  and
      Kondo, Risa  and
      Kajiwara, Tomoyuki",
    editor = "T.Y.S.S., Santosh  and
      Rodriguez, Juan Diego  and
      de Gibert, Ona",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-srw.102/",
    pages = "1169--1176",
    ISBN = "979-8-89176-393-7",
    abstract = "We disentangle multilingual sentence embeddings into language-dependent and language-agnostic components, leveraging the latter to improve cross-lingual similarity estimation.Previous studies focused on encoder-based approaches that use only the input sentence; in contrast, this study examines the effectiveness of disentanglement methods across a broader range of sentence embeddings, including decoder-based approaches and those that utilize prompts.Experimental results demonstrate that embedding disentanglement is effective for a wide variety of sentence embeddings."
}