跨语言情感分析新方法，让非英语文本也能精准识别观点。
Zero-Shot to Full-Resource: Cross-lingual Transfer Strategies for Aspect-Based Sentiment Analysis
- 用多语言迁移、混写和机器翻译提升非英语情感分析性能。
- 大模型微调在复杂任务中表现最佳，小模型适合混写策略。
- 新增德语数据集，推动非英语领域研究发展。
基于方面的情感分析（ABSA）可提取文本中针对特定方面的细粒度观点，但目前仍以英语为主。本文系统评估了七种语言（英语、德语、法语、荷兰语、俄语、西班牙语、捷克语）下最先进的ABSA方法，涵盖四个子任务（ACD、ACSA、TASD、ASQP）。在零资源、仅数据、全资源三种设置下，对比不同Transformer架构的表现，采用跨语言迁移、代码混写和机器翻译等策略。微调的大语言模型在整体表现上领先，尤其在生成类任务中；少样本情形下，其性能接近全量数据表现，而小型编码器模型在简单任务中依然有竞争力。跨语言训练多个非目标语言对微调大模型的迁移效果最强，而小型编码器或序列到序列模型则更受益于代码混写，凸显了不同架构适配的跨语言策略。此外，本文还构建了两个新的德语数据集：经调整的GERestaurant和首个德语ASQP数据集GERest，以推动非英语环境下的多语言ABSA研究。
原文摘要 · Abstract (English)
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.
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