Multi-Scale and Multi-Objective Optimization for Cross-Lingual Aspect-Based Sentiment Analysis

• URL: http://arxiv.org/abs/2502.13718v1
• Date: Wed, 19 Feb 2025 13:43:33 GMT
• Title: Multi-Scale and Multi-Objective Optimization for Cross-Lingual Aspect-Based Sentiment Analysis
• Authors: Chengyan Wu, Bolei Ma, Ningyuan Deng, Yanqing He, Yun Xue,
• Abstract summary: We propose a novel framework, Multi-Scale and Multi-Objective optimization (MSMO) for cross-lingual ABSA. We achieve cross-lingual sentence-level and aspect-level alignment, aligning features of aspect terms in different contextual environments. Results show that MSMO significantly enhances cross-lingual ABSA by achieving state-of-the-art performance across multiple languages and models.
• Score: 0.808899919316203
• License:
• Abstract: Aspect-based sentiment analysis (ABSA) is a sequence labeling task that has garnered growing research interest in multilingual contexts. However, recent studies lack more robust feature alignment and finer aspect-level alignment. In this paper, we propose a novel framework, Multi-Scale and Multi-Objective optimization (MSMO) for cross-lingual ABSA. During multi-scale alignment, we achieve cross-lingual sentence-level and aspect-level alignment, aligning features of aspect terms in different contextual environments. Specifically, we introduce code-switched bilingual sentences into the language discriminator and consistency training modules to enhance the model's robustness. During multi-objective optimization, we design two optimization objectives: supervised training and consistency training, aiming to enhance cross-lingual semantic alignment. To further improve model performance, we incorporate distilled knowledge of the target language into the model. Results show that MSMO significantly enhances cross-lingual ABSA by achieving state-of-the-art performance across multiple languages and models.

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