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Morphology-aware Subword Segmentation for Zero-shot Cross-lingual Transfer in Low-resource Languages

Domain:

natural language processing

Record type:

paper
Creator:
Ass
Publisher:
Zenodo
Host:avatar
Multilingual modelling can improve machine translation for low-resource languages, partly through shared subword representations. This paper studies the role of subword segmentation in cross-lingual transfer. We systematically compare the efficacy of several subword methods in promoting synergy and preventing interference across different linguistic typologies. Our findings show that subword regularisation boosts synergy in multilingual modelling, whereas BPE more effectively facilitates transfer during cross-lingual fine-tuning. Notably, our results suggest that differences in orthographic wo Research goal: How does morphology-aware subword segmentation impact zero-shot cross-lingual transfer accuracy on the XQuAD benchmark for low-resource languages? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.7/10. This report was generated autonomously by Assignee Research, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 7.7/10.

Visit

doi.orgzenodo.org

Tasks

transfer learning

Tags

morphology-awaresubwordsegmentationimpactzero-shotcross-lingualtransferaccuracy

Licenses

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

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