基于序列到序列模型的少样本跨语言方面级情感分析
Few-shot Cross-lingual Aspect-Based Sentiment Analysis with Sequence-to-Sequence Models
AI总结：
本文评估了在ABSA任务中向训练集添加少量目标语言示例的效果，发现仅10个示例即可显著提升跨语言性能，1000个示例甚至能超越单语基线。
摘要：
方面级情感分析（ABSA）在英语中受到了广泛关注，但由于标注数据的稀缺，低资源语言仍面临挑战。当前的跨语言ABSA方法通常依赖外部翻译工具，并忽略了将少量目标语言示例纳入训练的潜在益处。本文评估了在四个ABSA任务、六种目标语言和两种序列到序列模型中，向训练集添加少样本目标语言示例的效果。我们表明，仅添加十个目标语言示例就能显著提升零样本设置下的性能，并在减少预测错误方面实现与受限解码相似的效果。此外，我们证明将1,000个目标语言示例与英语数据结合，甚至能超越单语基线。这些发现为在低资源和特定领域设置下改进跨语言ABSA提供了实用见解，因为获取十个高质量标注示例既可行又高效。
英文摘要：
Aspect-based sentiment analysis (ABSA) has received substantial attention in English, yet challenges remain for low-resource languages due to the scarcity of labelled data. Current cross-lingual ABSA approaches often rely on external translation tools and overlook the potential benefits of incorporating a small number of target language examples into training. In this paper, we evaluate the effect of adding few-shot target language examples to the training set across four ABSA tasks, six target languages, and two sequence-to-sequence models. We show that adding as few as ten target language examples significantly improves performance over zero-shot settings and achieves a similar effect to constrained decoding in reducing prediction errors. Furthermore, we demonstrate that combining 1,000 target language examples with English data can even surpass monolingual baselines. These findings offer practical insights for improving cross-lingual ABSA in low-resource and domain-specific settings, as obtaining ten high-quality annotated examples is both feasible and highly effective.
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。
