Contrastive Learning Pre-Training and Quantum Theory for Cross-Lingual Aspect-Based Sentiment Analysis.

Area of Science:

• Natural Language Processing
• Computational Linguistics
• Artificial Intelligence

Background:

• Cross-lingual aspect-based sentiment analysis (ABSA) is challenging due to linguistic gaps between high-resource source and low-resource target languages.
• Existing methods often rely on multilingual pre-trained language models (mPLMs) and translation, but overlook factors beyond semantic similarity, limiting performance.
• Effective knowledge transfer across languages is crucial for accurate ABSA in diverse linguistic contexts.

Purpose of the Study:

• To propose a novel framework, CLQT, for enhancing cross-lingual ABSA performance.
• To address limitations of existing methods by incorporating factors beyond semantic similarity.
• To improve knowledge transfer and classification accuracy in low-resource target languages.

Main Methods:

• Developed a contrastive learning strategy to align data between source and target languages.
• Incorporated a quantum network utilizing quantum projection and entanglement for cross-lingual knowledge transfer.
• Combined contrastive learning pre-training with quantum theory for a unified framework (CLQT).

Main Results:

• The CLQT framework achieved strong results in cross-lingual ABSA tasks.
• Demonstrated a beneficial overall influence on classification performance in target languages.
• Experimental results validated the effectiveness of the proposed contrastive learning and quantum network integration.

Conclusions:

• The CLQT framework offers a promising approach to overcome challenges in cross-lingual ABSA.
• Integrating quantum theory provides novel mechanisms for effective cross-lingual knowledge transfer.
• The study highlights the potential of combining advanced machine learning techniques with quantum principles for NLP tasks.