论文标题
作者分析的零且几乎没有学习
Zero and Few-shot Learning for Author Profiling
论文作者
论文摘要
作者通过分析人们如何共享语言来分析作者特征。在这项工作中，我们从低资源的角度研究了该任务：使用很少或没有培训数据。我们根据需要探索不同的零和几射击模型，并在西班牙语和英语的几个分析任务上评估我们的系统。此外，我们研究了需要假设的效果和少量训练样本的大小。我们发现，基于Roberta-XLM的基于索赔的模型超出了受监督的文本分类器，并且我们可以平均使用少于50 \％的培训数据达到以前方法的80％的准确性。
Author profiling classifies author characteristics by analyzing how language is shared among people. In this work, we study that task from a low-resource viewpoint: using little or no training data. We explore different zero and few-shot models based on entailment and evaluate our systems on several profiling tasks in Spanish and English. In addition, we study the effect of both the entailment hypothesis and the size of the few-shot training sample. We find that entailment-based models out-perform supervised text classifiers based on roberta-XLM and that we can reach 80% of the accuracy of previous approaches using less than 50\% of the training data on average.
