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    <abstract>Recent multilingual pre-trained language models have achieved remarkable zero-shot performance, where the model is only finetuned on one source language and directly evaluated on target languages. In this work, we propose a self-learning framework that further utilizes unlabeled data of target languages, combined with uncertainty estimation in the process to select high-quality silver labels. Three different uncertainties are adapted and analyzed specifically for the cross lingual transfer: Language Heteroscedastic/Homoscedastic Uncertainty (LEU/LOU), Evidential Uncertainty (EVI). We evaluate our framework with uncertainties on two cross-lingual tasks including Named Entity Recognition (NER) and Natural Language Inference (NLI) covering 40 languages in total, which outperforms the baselines significantly by 10 F1 for NER on average and 2.5 accuracy for NLI.</abstract>
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%0 Conference Proceedings
%T Boosting Cross-Lingual Transfer via Self-Learning with Uncertainty Estimation
%A Xu, Liyan
%A Zhang, Xuchao
%A Zhao, Xujiang
%A Chen, Haifeng
%A Chen, Feng
%A Choi, Jinho D.
%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 xu-etal-2021-boosting
%X Recent multilingual pre-trained language models have achieved remarkable zero-shot performance, where the model is only finetuned on one source language and directly evaluated on target languages. In this work, we propose a self-learning framework that further utilizes unlabeled data of target languages, combined with uncertainty estimation in the process to select high-quality silver labels. Three different uncertainties are adapted and analyzed specifically for the cross lingual transfer: Language Heteroscedastic/Homoscedastic Uncertainty (LEU/LOU), Evidential Uncertainty (EVI). We evaluate our framework with uncertainties on two cross-lingual tasks including Named Entity Recognition (NER) and Natural Language Inference (NLI) covering 40 languages in total, which outperforms the baselines significantly by 10 F1 for NER on average and 2.5 accuracy for NLI.
%R 10.18653/v1/2021.emnlp-main.538
%U https://aclanthology.org/2021.emnlp-main.538
%U https://doi.org/10.18653/v1/2021.emnlp-main.538
%P 6716-6723

Markdown (Informal)

[Boosting Cross-Lingual Transfer via Self-Learning with Uncertainty Estimation](https://aclanthology.org/2021.emnlp-main.538) (Xu et al., EMNLP 2021)

• Boosting Cross-Lingual Transfer via Self-Learning with Uncertainty Estimation (Xu et al., EMNLP 2021)

ACL

• Liyan Xu, Xuchao Zhang, Xujiang Zhao, Haifeng Chen, Feng Chen, and Jinho D. Choi. 2021. Boosting Cross-Lingual Transfer via Self-Learning with Uncertainty Estimation. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 6716–6723, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.