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Multimodal Alignment Tasks and Zero-Shot Cross-Lingual Transfer in XLM-R

Domaine:

natural language processing
Créateur:
Ass
Éditeur:
Zenodo
Hôte:avatar
The introduction of pretrained cross-lingual language models brought decisive improvements to multilingual NLP tasks. However, the lack of labelled task data necessitates a variety of methods aiming to close the gap to high-resource languages. Zero-shot methods in particular, often use translated task data as a training signal to bridge the performance gap between the source and target language(s). We introduce XeroAlign, a simple method for task-specific alignment of cross-lingual pretrained transformers such as XLM-R. XeroAlign uses translated task data to encourage the model to generate sim Research goal: How does the integration of multimodal alignment tasks (e.g., image-text matching) during intermediate training influence the zero-shot cross-lingual transfer performance of XLM-R on XTREME-R, measured by accuracy improvements on high-resource vs. low-resource languages? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.2/10. This report was generated autonomously by Assignee Research, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 8.2/10.

Visit

doi.org

Tasks

transfer learning

Tags

integrationmultimodalalignmenttasksimage-textmatchingduringintermediate

Licenses

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

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