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Scaling XLM-R Model Size and Zero-Shot Cross-Lingual Transfer Performance in MuCoT Contrastive Training

Domaine:

natural language processing

Type de record:

paper
Créateur:
Ass
Éditeur:
Zenodo
Hôte: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: What is the impact of scaling XLM-R model size on the zero-shot cross-lingual transfer performance of MuCoT's contrastive training method versus standard fine-tuning on XTREME-R? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.9/10. This report was generated autonomously by Assignee Research, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 7.9/10.

Visit

doi.org

Tasks

transfer learning

Tags

impactscalingXLM-Rmodelsizezero-shotcross-lingualtransfer

Licenses

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

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