Logo Lanfrica
  • Home
  • Atlas
  • Insights
  • Docs
  • Sign in

© 2026 Lanfrica. All rights reserved. All copyrights of the resources shown on the Lanfrica website belong to the original copyright holders, unless explicitly stated otherwise.

MuCoT contrastive training vs. standard fine-tuning for zero-shot cross-lingual transfer in low-resource languages

Domain:

natural language processing

Record type:

paper
Creator:
Ass
Publisher:
Zenodo
Host:avatar
Pre-trained multilingual language encoders, such as multilingual BERT and XLM-R, show great potential for zero-shot cross-lingual transfer. However, these multilingual encoders do not precisely align words and phrases across languages. Especially, learning alignments in the multilingual embedding space usually requires sentence-level or word-level parallel corpora, which are expensive to be obtained for low-resource languages. An alternative is to make the multilingual encoders more robust; when fine-tuning the encoder using downstream task, we train the encoder to tolerate noise in the contex Research goal: How does MuCoT's contrastive training method compare to standard fine-tuning in improving zero-shot cross-lingual transfer performance on XTREME-R for low-resource languages when using XLM-R as the base model? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.1/10. This report was generated autonomously by Assignee Research, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 8.1/10.

Visit

doi.org

Tasks

transfer learning

Tags

MuCoTcontrastivetrainingmethodstandardfine-tuningimprovingzero-shot

Licenses

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

Similar

Synergistic Hybrid Batch Training vs. Standard Multilingual Fine-Tuning for Zero-Shot Cross-Lingual Retrieval in Low-ResourceContrastive Multimodal Pre-training for Zero-Shot Cross-Lingual Transfer in Low-Resource LanguagesEarly-Layer LoRA Adaptation vs Full Fine-Tuning for Zero-Shot Cross-Lingual Transfer in Low-Resource African LanguagesZero-shot Cross-lingual Transfer Performance in Low-resource Languages: Non-English vs. English Intermediate Task Fine-tuningDomain-Adaptive Fine-Tuning vs. Hybrid Batch Training in Zero-Shot Cross-Lingual Retrieval for Low-Resource XQuAD LanguagesHybrid Batch Training vs. Contrastive Learning for Zero-Shot Cross-Lingual Retrieval in Low-Resource Languages

Synergistic Hybrid Batch Training vs. Standard Multilingual Fine-Tuning for Zero-Shot Cross-Lingual Retrieval in Low-Resource

Information retrieval across different languages is an increasingly important challenge in natural l

Contrastive Multimodal Pre-training for Zero-Shot Cross-Lingual Transfer in Low-Resource Languages

This paper studies zero-shot cross-lingual transfer of vision-language models. Specifically, we focu

Early-Layer LoRA Adaptation vs Full Fine-Tuning for Zero-Shot Cross-Lingual Transfer in Low-Resource African Languages

Large Language Models (LLMs) have demonstrated remarkable capabilities, yet their performance in low

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

Domain-Adaptive Fine-Tuning vs. Hybrid Batch Training in Zero-Shot Cross-Lingual Retrieval for Low-Resource XQuAD Languages

Information retrieval across different languages is an increasingly important challenge in natural l

Hybrid Batch Training vs. Contrastive Learning for Zero-Shot Cross-Lingual Retrieval in Low-Resource Languages

Information retrieval across different languages is an increasingly important challenge in natural l