@inproceedings{ji-etal-2023-isotropic,
title = "Isotropic Representation Can Improve Zero-Shot Cross-Lingual Transfer on Multilingual Language Models",
author = "Ji, Yixin and
Wang, Jikai and
Li, Juntao and
Ye, Hai and
Zhang, Min",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.545",
doi = "10.18653/v1/2023.findings-emnlp.545",
pages = "8104--8118",
abstract = "With the development of multilingual pre-trained language models (mPLMs), zero-shot cross-lingual transfer shows great potential. To further improve the performance of cross-lingual transfer, many studies have explored representation misalignment caused by morphological differences but neglected the misalignment caused by the anisotropic distribution of contextual representations. In this work, we propose enhanced isotropy and constrained code-switching for zero-shot cross-lingual transfer to alleviate the problem of misalignment caused by the anisotropic representations and maintain syntactic structural knowledge. Extensive experiments on three zero-shot cross-lingual transfer tasks demonstrate that our method gains significant improvements over strong mPLM backbones and further improves the state-of-the-art methods.",
}
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<abstract>With the development of multilingual pre-trained language models (mPLMs), zero-shot cross-lingual transfer shows great potential. To further improve the performance of cross-lingual transfer, many studies have explored representation misalignment caused by morphological differences but neglected the misalignment caused by the anisotropic distribution of contextual representations. In this work, we propose enhanced isotropy and constrained code-switching for zero-shot cross-lingual transfer to alleviate the problem of misalignment caused by the anisotropic representations and maintain syntactic structural knowledge. Extensive experiments on three zero-shot cross-lingual transfer tasks demonstrate that our method gains significant improvements over strong mPLM backbones and further improves the state-of-the-art methods.</abstract>
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%0 Conference Proceedings
%T Isotropic Representation Can Improve Zero-Shot Cross-Lingual Transfer on Multilingual Language Models
%A Ji, Yixin
%A Wang, Jikai
%A Li, Juntao
%A Ye, Hai
%A Zhang, Min
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F ji-etal-2023-isotropic
%X With the development of multilingual pre-trained language models (mPLMs), zero-shot cross-lingual transfer shows great potential. To further improve the performance of cross-lingual transfer, many studies have explored representation misalignment caused by morphological differences but neglected the misalignment caused by the anisotropic distribution of contextual representations. In this work, we propose enhanced isotropy and constrained code-switching for zero-shot cross-lingual transfer to alleviate the problem of misalignment caused by the anisotropic representations and maintain syntactic structural knowledge. Extensive experiments on three zero-shot cross-lingual transfer tasks demonstrate that our method gains significant improvements over strong mPLM backbones and further improves the state-of-the-art methods.
%R 10.18653/v1/2023.findings-emnlp.545
%U https://aclanthology.org/2023.findings-emnlp.545
%U https://doi.org/10.18653/v1/2023.findings-emnlp.545
%P 8104-8118
Markdown (Informal)
[Isotropic Representation Can Improve Zero-Shot Cross-Lingual Transfer on Multilingual Language Models](https://aclanthology.org/2023.findings-emnlp.545) (Ji et al., Findings 2023)
ACL