从零样本到全资源：面向方面情感分析的跨语言迁移策略
Zero-Shot to Full-Resource: Cross-lingual Transfer Strategies for Aspect-Based Sentiment Analysis
AI总结：
本文评估了七种语言的ABSA方法，探讨了不同Transformer架构在零资源、数据仅用和全资源设置下的表现，发现大语言模型在复杂任务中表现最佳，同时提出两个新的德语数据集以促进多语言ABSA研究。
摘要：
面向方面的情感分析（ABSA）提取文本中针对特定方面的细粒度意见，但尽管在基于Transformer和指令调优模型上取得了重大进展，仍主要聚焦于英语。本文对七种语言（英语、德语、法语、荷兰语、俄语、西班牙语和捷克语）和四个子任务（ACD、ACSA、TASD、ASQP）的最先进ABSA方法进行了多语言评估。我们系统比较了不同Transformer架构在零资源、数据仅用和全资源设置下的表现，利用跨语言迁移、代码切换和机器翻译。微调的大语言模型（LLMs）在整体得分上最高，特别是在复杂生成任务中，而少样本模型在更简单的设置中接近此性能，其中较小的编码器模型也保持竞争力。在多个非目标语言上进行跨语言训练为微调的LLMs带来了最强的迁移效果，而较小的编码器或序列到序列模型则从代码切换中受益最大，突显了多语言ABSA中的架构特定策略。我们还贡献了两个新的德语数据集，一个改编的GERestaurant和第一个德语ASQP数据集（GERest），以鼓励多语言ABSA研究超越英语。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
