Marvelous Agglutinative Language Effect on Cross Lingual Transfer Learning

TL;DR

This paper reveals that incorporating agglutinative languages like Korean enhances cross-lingual transfer learning, challenging the traditional focus on language similarity for multilingual models.

Contribution

It demonstrates that agglutinative languages can improve transfer learning effectiveness, suggesting a new strategy for multilingual model training.

Findings

Agglutinative languages improve transfer learning performance.

Using Korean enhances cross-lingual transfer.

Challenges traditional language similarity assumptions.

Abstract

As for multilingual language models, it is important to select languages for training because of the curse of multilinguality. It is known that using languages with similar language structures is effective for cross lingual transfer learning. However, we demonstrate that using agglutinative languages such as Korean is more effective in cross lingual transfer learning. This is a great discovery that will change the training strategy of cross lingual transfer learning.