@inproceedings{tufa-etal-2024-unknown,
title = "Unknown Script: Impact of Script on Cross-Lingual Transfer",
author = "Tufa, Wondimagegnhue and
Markov, Ilia and
Vossen, Piek",
editor = "Cao, Yang (Trista) and
Papadimitriou, Isabel and
Ovalle, Anaelia",
booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.naacl-srw.14",
pages = "124--129",
abstract = "Cross-lingual transfer has become an effective way of transferring knowledge between languages. In this paper, we explore an often overlooked aspect in this domain: the influence of the source language of a language model on language transfer performance. We consider a case where the target language and its script are not part of the pre-trained model. We conduct a series of experiments on monolingual and multilingual models that are pre-trained on different tokenization methods to determine factors that affect cross-lingual transfer to a new language with a unique script. Our findings reveal the importance of the tokenizer as a stronger factor than the shared script, language similarity, and model size.",
}
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%0 Conference Proceedings
%T Unknown Script: Impact of Script on Cross-Lingual Transfer
%A Tufa, Wondimagegnhue
%A Markov, Ilia
%A Vossen, Piek
%Y Cao, Yang (Trista)
%Y Papadimitriou, Isabel
%Y Ovalle, Anaelia
%S Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F tufa-etal-2024-unknown
%X Cross-lingual transfer has become an effective way of transferring knowledge between languages. In this paper, we explore an often overlooked aspect in this domain: the influence of the source language of a language model on language transfer performance. We consider a case where the target language and its script are not part of the pre-trained model. We conduct a series of experiments on monolingual and multilingual models that are pre-trained on different tokenization methods to determine factors that affect cross-lingual transfer to a new language with a unique script. Our findings reveal the importance of the tokenizer as a stronger factor than the shared script, language similarity, and model size.
%U https://aclanthology.org/2024.naacl-srw.14
%P 124-129
Markdown (Informal)
[Unknown Script: Impact of Script on Cross-Lingual Transfer](https://aclanthology.org/2024.naacl-srw.14) (Tufa et al., NAACL 2024)
ACL
- Wondimagegnhue Tufa, Ilia Markov, and Piek Vossen. 2024. Unknown Script: Impact of Script on Cross-Lingual Transfer. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop), pages 124–129, Mexico City, Mexico. Association for Computational Linguistics.