Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages
Wietse de Vries, Martijn Wieling, Malvina Nissim
Abstract
Cross-lingual transfer learning with large multilingual pre-trained models can be an effective approach for low-resource languages with no labeled training data. Existing evaluations of zero-shot cross-lingual generalisability of large pre-trained models use datasets with English training data, and test data in a selection of target languages. We explore a more extensive transfer learning setup with 65 different source languages and 105 target languages for part-of-speech tagging. Through our analysis, we show that pre-training of both source and target language, as well as matching language families, writing systems, word order systems, and lexical-phonetic distance significantly impact cross-lingual performance. The findings described in this paper can be used as indicators of which factors are important for effective zero-shot cross-lingual transfer to zero- and low-resource languages.
BibTeX
@inproceedings{de-vries-etal-2022-make,
title = "Make the Best of Cross-lingual Transfer: Evidence from {POS} Tagging with over 100 Languages",
author = "de Vries, Wietse and
Wieling, Martijn and
Nissim, Malvina",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.529/",
doi = "10.18653/v1/2022.acl-long.529",
pages = "7676--7685"
}