Multi-level multilingual semantic alignment for zero-shot cross-lingual transfer learning.

Area of Science:

• Natural Language Processing
• Artificial Intelligence
• Computational Linguistics

Background:

• Cross-lingual transfer learning is crucial for low-resource languages.
• Existing methods often use single-level alignment (word or sentence) with pre-trained language models.
• This single-level approach overlooks hierarchical semantic correlations, hindering performance.

Purpose of the Study:

• To propose a novel multi-level alignment framework for cross-lingual transfer learning.
• To address the limitations of single-level alignment in capturing hierarchical semantic information.
• To improve performance on downstream tasks for low-resource languages.

Main Methods:

• Developed a multi-level alignment framework that hierarchically learns semantic correlations.
• Designed specific alignment training tasks to capture relationships between different linguistic levels (e.g., sentence-to-word).
• Incorporated an attention-based fusion mechanism (AFM) to integrate information from higher semantic levels.

Main Results:

• The proposed multi-level alignment framework significantly improves cross-lingual transfer learning.
• Experiments on text classification, paraphrase identification, and named entity recognition demonstrate effectiveness.
• The model achieved state-of-the-art performance across various benchmarks compared to strong baselines.

Conclusions:

• Multi-level alignment is superior to single-level alignment for cross-lingual transfer learning.
• The attention-based fusion mechanism effectively leverages hierarchical semantic information.
• The proposed framework offers a promising direction for advancing natural language processing in low-resource settings.