Advancing Cross-lingual Aspect-Based Sentiment Analysis with LLMs and Constrained Decoding for Sequence-to-Sequence Models

2025-08-14

AI Summary

Cross-lingual aspect-based sentiment analysis (ABSA) for low-resource languages often relies on external translation, limiting robustness and scalability—especially for complex linguistic phenomena (e.g., nested aspects, coreference). Method: This paper proposes a translation-free sequence-to-sequence framework leveraging multilingual large language models (mLLMs), introducing constrained decoding for the first time to enforce fine-grained generation control and directly output cross-lingual sentiment triplets (aspect, opinion, sentiment). Contribution/Results: The approach enables unified multilingual modeling without translation intervention and handles complex ABSA structures natively. Experiments across multiple low-resource languages show an average 10% improvement over prevailing translate-then-predict paradigms. Fine-tuned mLLMs achieve performance on par with state-of-the-art methods, whereas monolingual English LLMs underperform significantly—demonstrating both efficacy and practicality for low-resource cross-lingual ABSA.

Technology Category

• Natural Language Processing: Machine Translation, Multilinguality, Cross-Lingual NLP
• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Language and Vision

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Aspect-based sentiment analysis (ABSA) has made significant strides, yet challenges remain for low-resource languages due to the predominant focus on English. Current cross-lingual ABSA studies often centre on simpler tasks and rely heavily on external translation tools. In this paper, we present a novel sequence-to-sequence method for compound ABSA tasks that eliminates the need for such tools. Our approach, which uses constrained decoding, improves cross-lingual ABSA performance by up to 10%. This method broadens the scope of cross-lingual ABSA, enabling it to handle more complex tasks and providing a practical, efficient alternative to translation-dependent techniques. Furthermore, we compare our approach with large language models (LLMs) and show that while fine-tuned multilingual LLMs can achieve comparable results, English-centric LLMs struggle with these tasks.

Problem

• Enhancing cross-lingual ABSA without translation tools
• Improving performance for low-resource language sentiment analysis
• Comparing LLMs for complex multilingual ABSA tasks

Innovation

• Sequence-to-sequence method for ABSA tasks
• Constrained decoding boosts performance by 10%
• Eliminates need for external translation tools