A Cross Language Transfer Learning Algorithm for French Corpus Based on Knowledge Distillation

With the deepening growth of globalization, language barriers have become a major challenge in information exchange. Cross Language Transfer (CLT) learning aims to address this issue by enabling machines to understand and generate texts in different languages. Transfer learning is currently a hot research field in machine learning (ML), which utilizes source domain knowledge related to the target domain to assist in learning the target domain. CLT aims to learn corresponding tasks in the target language using annotated samples from the source language, and is an important way to solve the problem of insufficient labeled data in the target language. and is an important way to solve the problem of insufficient labeled data in the target language. As one of the internationally recognized languages, studying CLT learning algorithms based on French is of great significance. Knowledge distillation (KD) is a method of transferring knowledge from one model to another, which has achieved great success in challenging transfer learning tasks. This article designs a KD based French corpus CLT learning algorithm, which transfers knowledge from the teacher model to the student model and introduces corpora from other languages for CLT learning. The experimental results show that the algorithm proposed in this paper has significant advantages in CLT learning and provides an effective solution for solving language barriers.