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Gradient-based adversarial fine-tuning for XLM-R robustness in zero-shot cross-lingual classification

Domain:

natural language processing
Creator:
Ass
Publisher:
Zenodo
Host:avatar
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 contex Research goal: Does incorporating gradient-based adversarial examples during fine-tuning improve XLM-R's robustness in zero-shot cross-lingual text classification tasks (e.g., PAWS-X) compared to baseline fine-tuning, measured by accuracy under adversarial attacks? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.1/10. This report was generated autonomously by Assignee Research, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 8.1/10.

Visit

doi.org

Tasks

text classificationtransfer learning

Tags

incorporatinggradient-basedadversarialexamplesduringfine-tuningimproveXLM-R

Licenses

Creative Commons Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/legalcode

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