@inproceedings{mao-etal-2024-overcoming,
    title = "Overcoming Early Saturation on Low-Resource Languages in Multilingual Dependency Parsing",
    author = "Mao, Jiannan  and
      Ding, Chenchen  and
      Kaing, Hour  and
      Tanaka, Hideki  and
      Utiyama, Masao  and
      Matsumoto., Tadahiro",
    editor = {Bhatia, Archna  and
      Bouma, Gosse  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Evang, Kilian  and
      Garcia, Marcos  and
      Giouli, Voula  and
      Han, Lifeng  and
      Nivre, Joakim  and
      Rademaker, Alexandre},
    booktitle = "Proceedings of the Joint Workshop on Multiword Expressions and Universal Dependencies (MWE-UD) @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.mwe-1.10",
    pages = "63--69",
    abstract = "UDify is a multilingual and multi-task parser fine-tuned on mBERT that achieves remarkable performance in high-resource languages. However, the performance saturates early and decreases gradually in low-resource languages as training proceeds. This work applies a data augmentation method and conducts experiments on seven few-shot and four zero-shot languages. The unlabeled attachment scores were improved on the zero-shot languages dependency parsing tasks, with the average score rising from 67.1{\%} to 68.7{\%}. Meanwhile, dependency parsing tasks for high-resource languages and other tasks were hardly affected. Experimental results indicate the data augmentation method is effective for low-resource languages in a multilingual dependency parsing.",
}

UDify is a multilingual and multi-task parser fine-tuned on mBERT that achieves remarkable performance in high-resource languages. However, the performance saturates early and decreases gradually in low-resource languages as training proceeds. This work applies a data augmentation method and conducts experiments on seven few-shot and four zero-shot languages. The unlabeled attachment scores were improved on the zero-shot languages dependency parsing tasks, with the average score rising from 67.1{\%} to 68.7{\%}. Meanwhile, dependency parsing tasks for high-resource languages and other tasks were hardly affected. Experimental results indicate the data augmentation method is effective for low-resource languages in a multilingual dependency parsing.
Mao, Jiannan Ding, Chenchen Kaing, Hour Tanaka, Hideki Utiyama, Masao Matsumoto., Tadahiro
Overcoming Early Saturation on Low-Resource Languages in Multilingual Dependency Parsing