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.
Enhancing Cross-Lingual Transfer of Large Language Models via Self-Translation
Large language models can capture cross-lingual correspondence, which can be effectively elicited through self-translation to improve cross-lingual transfer performance.
