Authors:
Jakub Šmíd
1
;
2
;
Pavel Přibáň
1
and
Pavel Král
2
Affiliations:
1
Department of Computer Science and Engineering, University of West Bohemia in Pilsen, Univerzitní, Pilsen, Czech Republic
;
2
NTIS - New Technologies for the Information Society, University of West Bohemia in Pilsen, Univerzitní, Pilsen, Czech Republic
Keyword(s):
Cross-Lingual Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis, Large Language Models, Transformers, Constrained Decoding.
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.