@inproceedings{goworek-etal-2025-senwich,
    title = "{S}en{W}i{C}h: Sense-Annotation of Low-Resource Languages for {W}i{C} using Hybrid Methods",
    author = "Goworek, Roksana  and
      Karlcut, Harpal Singh  and
      Shezad, Hamza  and
      Darshana, Nijaguna  and
      Mane, Abhishek  and
      Bondada, Syam  and
      Sikka, Raghav  and
      Mammadov, Ulvi  and
      Allahverdiyev, Rauf  and
      Purighella, Sriram Satkirti  and
      Gupta, Paridhi  and
      Ndegwa, Muhinyia  and
      Tran, Bao Khanh  and
      Dubossarsky, Haim",
    editor = "Hahn, Michael  and
      Rani, Priya  and
      Kumar, Ritesh  and
      Shcherbakov, Andreas  and
      Sorokin, Alexey  and
      Serikov, Oleg  and
      Cotterell, Ryan  and
      Vylomova, Ekaterina",
    booktitle = "Proceedings of the 7th Workshop on Research in Computational Linguistic Typology and Multilingual NLP",
    month = aug,
    year = "2025",
    address = "Vinenna. Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.sigtyp-1.7/",
    pages = "61--74",
    ISBN = "979-8-89176-281-7",
    abstract = "This paper addresses the critical need for high-quality evaluation datasets in low-resource languages to advance cross-lingual transfer. While cross-lingual transfer offers a key strategy for leveraging multilingual pretraining to expand language technologies to understudied and typologically diverse languages, its effectiveness is dependent on quality and suitable benchmarks. We release new sense-annotated datasets of sentences containing polysemous words, spanning nine low-resource languages across diverse language families and scripts. To facilitate dataset creation, the paper presents a demonstrably beneficial semi-automatic annotation method. The utility of the datasets is demonstrated through Word-in-Context (WiC) formatted experiments that evaluate transfer on these low-resource languages. Results highlight the importance of targeted dataset creation and evaluation for effective polysemy disambiguation in low-resource settings and transfer studies. The released datasets and code aim to support further research into fair, robust, and truly multilingual NLP."
}