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.

Comparative Effects of Intermediate-Task Fine-Tuning on Cross-Lingual Alignment Metrics for Under-Represented Languages

Domaine:

natural language processing

Type de record:

paper
Créateur:
Ass
Éditeur:
Zenodo
Hôte:avatar
Euphemisms are culturally variable and often ambiguous, posing challenges for language models, especially in low-resource settings. This paper investigates how cross-lingual transfer via sequential fine-tuning affects euphemism detection across five languages: English, Spanish, Chinese, Turkish, and Yoruba. We compare sequential fine-tuning with monolingual and simultaneous fine-tuning using XLM-R and mBERT, analyzing how performance is shaped by language pairings, typological features, and pretraining coverage. Results show that sequential fine-tuning with a high-resource L1 improves L2 perfo Research goal: What is the comparative effect of English intermediate-task fine-tuning versus direct target-language fine-tuning on alignment metrics for under-represented languages in cross-lingual transfer scenarios? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.8/10. This report was generated autonomously by Assignee Research, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 7.8/10.

Visit

doi.orgzenodo.org

Tasks

transfer learning

Languages

Yoruba

Tags

comparativeeffectEnglishintermediate-taskfine-tuningversusdirecttarget-language

Licenses

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

Similaires

Intermediate Task Fine-Tuning for Cross-Lingual Euphemism Detection EfficiencySequential Fine-Tuning Order Effects on Cross-Lingual Euphemism Detection AlignmentComparative Analysis of Low-Resource versus English Intermediate Task Fine-Tuning for XNLI Zero-Shot Cross-Lingual TransferXLM-R Performance Enhancement via Intermediate Task Fine-Tuning for Cross-Lingual Euphemism DetectionCross-lingual Euphemism Detection Stability in Sequential Fine-tuning with Intermediate LanguagesZero-shot Cross-lingual Transfer Performance in Low-resource Languages: Non-English vs. English Intermediate Task Fine-tuning

Intermediate Task Fine-Tuning for Cross-Lingual Euphemism Detection Efficiency

Euphemisms are culturally variable and often ambiguous, posing challenges for language models, espec

Sequential Fine-Tuning Order Effects on Cross-Lingual Euphemism Detection Alignment

Euphemisms are culturally variable and often ambiguous, posing challenges for language models, espec

Comparative Analysis of Low-Resource versus English Intermediate Task Fine-Tuning for XNLI Zero-Shot Cross-Lingual Transfer

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

XLM-R Performance Enhancement via Intermediate Task Fine-Tuning for Cross-Lingual Euphemism Detection

Euphemisms are culturally variable and often ambiguous, posing challenges for language models, espec

Cross-lingual Euphemism Detection Stability in Sequential Fine-tuning with Intermediate Languages

Euphemisms are culturally variable and often ambiguous, posing challenges for language models, espec

Zero-shot Cross-lingual Transfer Performance in Low-resource Languages: Non-English vs. English Intermediate Task Fine-tuning

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