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Pre-training Data Size Variation in mT5 and Cross-Lingual Transfer Performance on the XTREME-R Benchmark

Domain:

natural language processing

Record type:

paper
Creator:
Ass
Publisher:
Zenodo
Host:avatar
Multi-lingual language models (LM), such as mBERT, XLM-R, mT5, mBART, have been remarkably successful in enabling natural language tasks in low-resource languages through cross-lingual transfer from high-resource ones. In this work, we try to better understand how such models, specifically mT5, transfer *any* linguistic and semantic knowledge across languages, even though no explicit cross-lingual signals are provided during pre-training. Rather, only unannotated texts from each language are presented to the model separately and independently of one another, and the model appears to implicitly Research goal: What is the impact of varying the pre-training data size for each language in mT5 on cross-lingual transfer performance in the XTREME-R benchmark, measured by accuracy and language-specific F1 scores? 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

language modelingtransfer learning

Tags

impactvaryingpre-trainingdatasizeeachlanguagemT5

Licenses

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

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