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            <namePart type="given">Xuanjing</namePart>
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    <abstract>Pre-trained multilingual language encoders, such as multilingual BERT and XLM-R, show great potential for zero-shot cross-lingual transfer. However, these multilingual encoders do not precisely align words and phrases across languages. Especially, learning alignments in the multilingual embedding space usually requires sentence-level or word-level parallel corpora, which are expensive to be obtained for low-resource languages. An alternative is to make the multilingual encoders more robust; when fine-tuning the encoder using downstream task, we train the encoder to tolerate noise in the contextual embedding spaces such that even if the representations of different languages are not aligned well, the model can still achieve good performance on zero-shot cross-lingual transfer. In this work, we propose a learning strategy for training robust models by drawing connections between adversarial examples and the failure cases of zero-shot cross-lingual transfer. We adopt two widely used robust training methods, adversarial training and randomized smoothing, to train the desired robust model. The experimental results demonstrate that robust training improves zero-shot cross-lingual transfer on text classification tasks. The improvement is more significant in the generalized cross-lingual transfer setting, where the pair of input sentences belong to two different languages.</abstract>
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%0 Conference Proceedings
%T Improving Zero-Shot Cross-Lingual Transfer Learning via Robust Training
%A Huang, Kuan-Hao
%A Ahmad, Wasi
%A Peng, Nanyun
%A Chang, Kai-Wei
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F huang-etal-2021-improving-zero
%X Pre-trained multilingual language encoders, such as multilingual BERT and XLM-R, show great potential for zero-shot cross-lingual transfer. However, these multilingual encoders do not precisely align words and phrases across languages. Especially, learning alignments in the multilingual embedding space usually requires sentence-level or word-level parallel corpora, which are expensive to be obtained for low-resource languages. An alternative is to make the multilingual encoders more robust; when fine-tuning the encoder using downstream task, we train the encoder to tolerate noise in the contextual embedding spaces such that even if the representations of different languages are not aligned well, the model can still achieve good performance on zero-shot cross-lingual transfer. In this work, we propose a learning strategy for training robust models by drawing connections between adversarial examples and the failure cases of zero-shot cross-lingual transfer. We adopt two widely used robust training methods, adversarial training and randomized smoothing, to train the desired robust model. The experimental results demonstrate that robust training improves zero-shot cross-lingual transfer on text classification tasks. The improvement is more significant in the generalized cross-lingual transfer setting, where the pair of input sentences belong to two different languages.
%R 10.18653/v1/2021.emnlp-main.126
%U https://aclanthology.org/2021.emnlp-main.126
%U https://doi.org/10.18653/v1/2021.emnlp-main.126
%P 1684-1697

Markdown (Informal)

[Improving Zero-Shot Cross-Lingual Transfer Learning via Robust Training](https://aclanthology.org/2021.emnlp-main.126) (Huang et al., EMNLP 2021)

• Improving Zero-Shot Cross-Lingual Transfer Learning via Robust Training (Huang et al., EMNLP 2021)

ACL

• Kuan-Hao Huang, Wasi Ahmad, Nanyun Peng, and Kai-Wei Chang. 2021. Improving Zero-Shot Cross-Lingual Transfer Learning via Robust Training. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 1684–1697, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.