首个面向捷克语的情感分析提示方法，效果优于传统微调。
Prompt-Based Approach for Czech Sentiment Analysis
- 用序列到序列模型统一处理情感分类与方面级任务
- 少样本场景下提示法比微调提升显著，零样本亦表现优异
- 目标领域预训练能大幅增强零样本性能，适合资源有限场景
本文首次提出针对捷克语方面级情感分析和情感分类的提示方法。采用序列到序列模型同时解决方面级任务，并证明提示方法在性能上优于传统微调。此外，我们在情感分类中进行了零样本和少样本学习实验，结果显示，在训练样本有限的情况下，提示法显著优于传统微调。我们还表明，在目标领域数据上进行预训练，可显著提升零样本场景下的表现。
原文摘要 · Abstract (English)
This paper introduces the first prompt-based methods for aspect-based sentiment analysis and sentiment classification in Czech. We employ the sequence-to-sequence models to solve the aspect-based tasks simultaneously and demonstrate the superiority of our prompt-based approach over traditional fine-tuning. In addition, we conduct zero-shot and few-shot learning experiments for sentiment classification and show that prompting yields significantly better results with limited training examples compared to traditional fine-tuning. We also demonstrate that pre-training on data from the target domain can lead to significant improvements in a zero-shot scenario.
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