Improving Generative Cross-lingual Aspect-Based Sentiment Analysis with Constrained Decoding

• URL: http://arxiv.org/abs/2508.10369v1
• Date: Thu, 14 Aug 2025 06:07:53 GMT
• Title: Improving Generative Cross-lingual Aspect-Based Sentiment Analysis with Constrained Decoding
• Authors: Jakub Šmíd, Pavel Přibáň, Pavel Král,
• Abstract summary: This paper introduces a novel approach using constrained decoding with sequence-to-sequence models.<n>It improves cross-lingual performance by 5% on average for the most complex task.<n>We evaluate our approach across seven languages and six ABSA tasks.
• Score: 0.8602553195689511
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: While aspect-based sentiment analysis (ABSA) has made substantial progress, challenges remain for low-resource languages, which are often overlooked in favour of English. Current cross-lingual ABSA approaches focus on limited, less complex tasks and often rely on external translation tools. This paper introduces a novel approach using constrained decoding with sequence-to-sequence models, eliminating the need for unreliable translation tools and improving cross-lingual performance by 5\% on average for the most complex task. The proposed method also supports multi-tasking, which enables solving multiple ABSA tasks with a single model, with constrained decoding boosting results by more than 10\%. We evaluate our approach across seven languages and six ABSA tasks, surpassing state-of-the-art methods and setting new benchmarks for previously unexplored tasks. Additionally, we assess large language models (LLMs) in zero-shot, few-shot, and fine-tuning scenarios. While LLMs perform poorly in zero-shot and few-shot settings, fine-tuning achieves competitive results compared to smaller multilingual models, albeit at the cost of longer training and inference times. We provide practical recommendations for real-world applications, enhancing the understanding of cross-lingual ABSA methodologies. This study offers valuable insights into the strengths and limitations of cross-lingual ABSA approaches, advancing the state-of-the-art in this challenging research domain.

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