评估和解释零样本跨语言新闻情感分析的训练策略
Evaluating and explaining training strategies for zero-shot cross-lingual news sentiment analysis
AI总结：
本文研究零样本跨语言新闻情感检测，提出利用段落级信息的新任务目标POA，并对比机器翻译、上下文学习等方法，证明POA能以更低计算开销取得有竞争力的性能提升。
摘要：
我们研究零样本跨语言新闻情感检测，旨在开发无需目标语言训练数据即可部署于多种语言的鲁棒情感分类器。我们在几种资源较少的语言中引入了新的评估数据集，并实验了一系列方法，包括使用机器翻译；利用大型语言模型进行上下文学习；以及各种中间训练机制，其中包括一种新颖的任务目标POA，该目标利用段落级信息。我们的结果表明，与现有最优水平相比有显著改进，其中上下文学习通常表现最佳，但新颖的POA方法以低得多的计算开销提供了一种有竞争力的替代方案。我们还表明，语言相似性本身不足以预测跨语言迁移的成功，而语义内容和结构上的相似性可能同样重要。
英文摘要：
We investigate zero-shot cross-lingual news sentiment detection, aiming to develop robust sentiment classifiers that can be deployed across multiple languages without target-language training data. We introduce novel evaluation datasets in several less-resourced languages, and experiment with a range of approaches including the use of machine translation; in-context learning with large language models; and various intermediate training regimes including a novel task objective, POA, that leverages paragraph-level information. Our results demonstrate significant improvements over the state of the art, with in-context learning generally giving the best performance, but with the novel POA approach giving a competitive alternative with much lower computational overhead. We also show that language similarity is not in itself sufficient for predicting the success of cross-lingual transfer, but that similarity in semantic content and structure can be equally important.
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。
