论文标题
仔细观察几次跨语言转移：镜头的选择很重要
A Closer Look at Few-Shot Crosslingual Transfer: The Choice of Shots Matters
论文作者
论文摘要
已经证明，很少有传动的跨语言转移与多语言Bert（如多语言BERT）的零镜头表现优于其零拍。尽管越来越受欢迎，但几乎没有关注标准化和分析几杆实验的设计。在这项工作中，我们重点介绍了这种缺点带来的基本风险，这表明该模型对选择少量镜头具有很高的敏感性。我们对40套不同的NLP任务进行了大规模的实验研究，对40种不同的NLP任务进行了抽样的镜头。我们提供了很少转移的成功和故障案例的分析，这突出了词汇特征的作用。此外，我们表明，一种直接的完整模型登录方法对于几次转移非常有效，表现优于几种最先进的几次方法。为了迈向标准化少量跨语言实验设计的一步，我们使我们的抽样镜头公开可用。
Few-shot crosslingual transfer has been shown to outperform its zero-shot counterpart with pretrained encoders like multilingual BERT. Despite its growing popularity, little to no attention has been paid to standardizing and analyzing the design of few-shot experiments. In this work, we highlight a fundamental risk posed by this shortcoming, illustrating that the model exhibits a high degree of sensitivity to the selection of few shots. We conduct a large-scale experimental study on 40 sets of sampled few shots for six diverse NLP tasks across up to 40 languages. We provide an analysis of success and failure cases of few-shot transfer, which highlights the role of lexical features. Additionally, we show that a straightforward full model finetuning approach is quite effective for few-shot transfer, outperforming several state-of-the-art few-shot approaches. As a step towards standardizing few-shot crosslingual experimental designs, we make our sampled few shots publicly available.
