论文标题
少量点：几次学习基准，以了解联合语言理解
FewJoint: A Few-shot Learning Benchmark for Joint Language Understanding
论文作者
论文摘要
几乎没有学习的学习（FSL）是机器学习的未来步骤之一，并且引起了很多关注。但是，与其他领域的快速发展（例如计算机视觉）相反，自然语言处理（NLP）中FSL的进步要慢得多。造成这种情况的关键原因之一是缺乏公共基准。 NLP FSL研究总是在其自身构造的几个数据集上报告新的结果，这在结果比较方面效率低下，因此阻碍了累积进展。在本文中，我们介绍了NLP的小巧学习基准。与大多数仅关注简单N分类问题的NLP FSL研究不同，我们的基准引入了很少的联合对话语言理解，这还涵盖了结构预测和多任务依赖问题。这使我们的基准可以反映出简单的n个分类超出现实字的NLP复杂性。我们的基准测试用于SMP2020-ECDT Task-1的几次学习竞赛。我们还提供了一个兼容的FSL平台来简化实验设置。
Few-shot learning (FSL) is one of the key future steps in machine learning and has raised a lot of attention. However, in contrast to the rapid development in other domains, such as Computer Vision, the progress of FSL in Nature Language Processing (NLP) is much slower. One of the key reasons for this is the lacking of public benchmarks. NLP FSL researches always report new results on their own constructed few-shot datasets, which is pretty inefficient in results comparison and thus impedes cumulative progress. In this paper, we present FewJoint, a novel Few-Shot Learning benchmark for NLP. Different from most NLP FSL research that only focus on simple N-classification problems, our benchmark introduces few-shot joint dialogue language understanding, which additionally covers the structure prediction and multi-task reliance problems. This allows our benchmark to reflect the real-word NLP complexity beyond simple N-classification. Our benchmark is used in the few-shot learning contest of SMP2020-ECDT task-1. We also provide a compatible FSL platform to ease experiment set-up.
