Abstract:Aspect-based Sentiment Analysis (ABSA) extracts fine-grained opinions toward specific aspects within text but remains largely English-focused despite major advances in transformer-based and instruction-tuned models. This work presents a multilingual evaluation of state-of-the-art ABSA approaches across seven languages (English, German, French, Dutch, Russian, Spanish, and Czech) and four subtasks (ACD, ACSA, TASD, ASQP). We systematically compare different transformer architectures under zero-resource, data-only, and full-resource settings, using cross-lingual transfer, code-switching and machine translation. Fine-tuned Large Language Models (LLMs) achieve the highest overall scores, particularly in complex generative tasks, while few-shot counterparts approach this performance in simpler setups, where smaller encoder models also remain competitive. Cross-lingual training on multiple non-target languages yields the strongest transfer for fine-tuned LLMs, while smaller encoder or seq-to-seq models benefit most from code-switching, highlighting architecture-specific strategies for multilingual ABSA. We further contribute two new German datasets, an adapted GERestaurant and the first German ASQP dataset (GERest), to encourage multilingual ABSA research beyond English.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2604.26619 [cs.CL] |
| (or arXiv:2604.26619v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2604.26619 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jakob Fehle [view email]
[v1]
Wed, 29 Apr 2026 12:45:25 UTC (426 KB)