Logo Lanfrica
  • Accueil
  • Atlas
  • Analyses
  • Documentation
  • Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

Cross-lingual Transfer Accuracy in Multilingual Models with Varied Low-Resource African Language Ratios

Domaine:

natural language processing
Créateur:
Ass
Éditeur:
Zenodo
Hôte: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 ratio of low-resource African languages to high-resource languages in pretraining on the cross-lingual transfer accuracy of multilingual models, as measured by the FLEURS benchmark? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.5/10. This report was generated autonomously by Assignee Research, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 7.5/10.

Visit

doi.org

Tasks

language modelingtransfer learning

Tags

impactvaryingratiolow-resourceAfricanlanguageshigh-resourcepretraining

Licenses

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

Similaires

Cross-lingual NER Transfer with Pretrained Language Models: Accuracy Degradation in Low-Resource LanguagesCross-lingual transfer of multilingual models on low resource African Languageskaranwxliaa/Cross-lingual-transfer-of-multilingual-models-on-low-resource-African-LanguagesCross-lingual STS Method vs Multilingual Language Models in Zero-shot Low-resource AccuracyImpact of Multilingual Intermediate Tasks on Zero-Shot Cross-Lingual Transfer Accuracy in Low-Resource LanguagesZero-shot cross-lingual transfer accuracy in low-resource languages: multilingual vs. English-only intermediate tasks

Cross-lingual NER Transfer with Pretrained Language Models: Accuracy Degradation in Low-Resource Languages

Multilingual Language Models (MLLMs) exhibit robust cross-lingual transfer capabilities, or the abil

Cross-lingual transfer of multilingual models on low resource African Languages

Large multilingual models have significantly advanced natural language processing (NLP) research. Ho

karanwxliaa/Cross-lingual-transfer-of-multilingual-models-on-low-resource-African-Languages

A comparison of Cross-lingual transfer of Transformer & Neural based: Multilingual & Monolingual mod

Cross-lingual STS Method vs Multilingual Language Models in Zero-shot Low-resource Accuracy

Pretrained multilingual language models have become a common tool in transferring NLP capabilities t

Impact of Multilingual Intermediate Tasks on Zero-Shot Cross-Lingual Transfer Accuracy in Low-Resource Languages

Intermediate-task training---fine-tuning a pretrained model on an intermediate task before fine-tuni

Zero-shot cross-lingual transfer accuracy in low-resource languages: multilingual vs. English-only intermediate tasks

Intermediate-task training---fine-tuning a pretrained model on an intermediate task before fine-tuni