@inproceedings{chersoni-etal-2020-automatic,
    title = "Automatic Learning of Modality Exclusivity Norms with Crosslingual Word Embeddings",
    author = "Chersoni, Emmanuele  and
      Xiang, Rong  and
      Lu, Qin  and
      Huang, Chu-Ren",
    editor = "Gurevych, Iryna  and
      Apidianaki, Marianna  and
      Faruqui, Manaal",
    booktitle = "Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Ingest-2025-COMPUTEL/2020.starsem-1.4/",
    pages = "32--38",
    abstract = "Collecting modality exclusivity norms for lexical items has recently become a common practice in psycholinguistics and cognitive research. However, these norms are available only for a relatively small number of languages and often involve a costly and time-consuming collection of ratings. In this work, we aim at learning a mapping between word embeddings and modality norms. Our experiments focused on crosslingual word embeddings, in order to predict modality association scores by training on a high-resource language and testing on a low-resource one. We ran two experiments, one in a monolingual and the other one in a crosslingual setting. Results show that modality prediction using off-the-shelf crosslingual embeddings indeed has moderate-to-high correlations with human ratings even when regression algorithms are trained on an English resource and tested on a completely unseen language."
}