@inproceedings{rice-etal-2025-untangling,
    title = "Untangling the Influence of Typology, Data, and Model Architecture on Ranking Transfer Languages for Cross-Lingual {POS} Tagging",
    author = "Rice, Enora  and
      Marashian, Ali  and
      Haynie, Hannah  and
      Wense, Katharina  and
      Palmer, Alexis",
    editor = "Nguyen, Duc",
    booktitle = "Proceedings of the 1st Workshop on Language Models for Underserved Communities (LM4UC 2025)",
    month = may,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Ingest-2025-COMPUTEL/2025.lm4uc-1.4/",
    pages = "22--31",
    ISBN = "979-8-89176-242-8",
    abstract = "Cross-lingual transfer learning is an invaluable tool for overcoming data scarcity, yet selecting a suitable transfer language remains a challenge. The precise roles of linguistic typology, training data, and model architecture in transfer language choice are not fully understood. We take a holistic approach, examining how both dataset-specific and fine-grained typological features influence transfer language selection for part-of-speech tagging, considering two different sources for morphosyntactic features. While previous work examines these dynamics in the context of bilingual biLSTMS, we extend our analysis to a more modern transfer learning pipeline: zero-shot prediction with pretrained multilingual models. We train a series of transfer language ranking systems and examine how different feature inputs influence ranker performance across architectures. Word overlap, type-token ratio, and genealogical distance emerge as top features across all architectures. Our findings reveal that a combination of typological and dataset-dependent features leads to the best rankings, and that good performance can be obtained with either feature group on its own."
}