The field of aspect-based sentiment analysis (ABSA) is rapidly advancing, with a growing focus on cross-lingual approaches. Recent research has explored the use of large language models (LLMs) and sequence-to-sequence models to improve performance in low-resource languages. The development of new datasets and evaluation frameworks has also enabled more accurate comparisons of different approaches. Notably, the use of constrained decoding and few-shot learning has shown promise in improving cross-lingual ABSA performance. Overall, the field is moving towards more sophisticated and efficient methods for analyzing sentiment in multiple languages.

Some noteworthy papers in this area include: Few-shot Cross-lingual Aspect-Based Sentiment Analysis with Sequence-to-Sequence Models, which demonstrates the effectiveness of adding few-shot target language examples to training sets. LACA: Improving Cross-lingual Aspect-Based Sentiment Analysis with LLM Data Augmentation, which proposes a novel approach using LLMs to generate high-quality pseudo-labelled data in target languages. Advancing Cross-lingual Aspect-Based Sentiment Analysis with LLMs and Constrained Decoding for Sequence-to-Sequence Models, which presents a novel sequence-to-sequence method that eliminates the need for external translation tools.