Cross-Lingual Transfer Learning

insight - Cross-Lingual Transfer Learning

DA-Net: Disentangled and Adaptive Network for Multi-Source Cross-Lingual Transfer Learning

The author proposes DA-Net to address challenges in multi-source cross-lingual transfer learning by introducing a Disentangled and Adaptive Network. The approach aims to purify input representations and align class-level distributions for improved model performance.

DA-Net: A Disentangled and Adaptive Network for Multi-Source Cross-Lingual Transfer Learning

提案されたDA-Netは、複数のソース言語からのクロスリンガルタスクにおけるモデルの性能を向上させる効果的な手法である。

Learning Transfer in Programming Languages: Extensive Study

Learning transfers well across several programming languages.

Cross-Lingual Learning vs. Low-Resource Fine-Tuning: A Case Study with Fact-Checking in Turkish

Die Studie untersucht die Wirksamkeit von Cross-Lingual Transfer Learning für Fact-Checking in der Türkei.

DA-Net: A Disentangled and Adaptive Network for Multi-Source Cross-Lingual Transfer Learning

다중 소스 크로스-언어 전이 학습을 위한 DA-Net의 효과적인 구조 소개

Measuring Cross-lingual Knowledge Transfer Across Diverse Languages

The study demonstrates that language models can effectively transfer knowledge across diverse languages, with the transfer being largely independent of language proximity. This suggests the presence of language-agnostic representations that enable cross-lingual generalization.

Measuring Catastrophic Forgetting in Cross-Lingual Classification Transfer Paradigms and Tuning Strategies

Cross-lingual transfer learning can lead to catastrophic forgetting of previously acquired knowledge in the source language. This study compares different cross-lingual transfer strategies and fine-tuning approaches to measure and mitigate this effect.

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