小样本学习(Few-Shot Learning,以下简称 FSL )用于解决当可用的数据量比较少时,如何提升神经网络的性能。在 FSL 中,经常用到的一类方法被称为 Meta-learning。和普通的神经网络的训练方法一样,Meta-learning 也包含训练过程和测试过程,但是它的训练过程被称作 Meta-training 和 Meta-testing。

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题目: Span-ConveRT: Few-shot Span Extraction for Dialog with Pretrained Conversational Representations

摘要:

本文介绍了Span-ConveRT,这是一种用于对话框槽填充的轻量级模型,它将任务描述为基于轮的span提取任务。这个公式允许简单地集成编码在大型预先训练的会话模型中的会话知识,如ConveRT (Henderson等人,2019)。我们展示了在Span-ConveRT中利用这些知识对于很少的学习场景特别有用:

  • 一个跨度提取器,在目标域从零开始训练表示,
  • 基于bert的跨度提取器。

为了激发更多关于填槽任务的span提取的工作,我们还发布了RESTAURANTS-8K,这是一个新的具有挑战性的数据集,包含8,198个话语,是从餐馆预订领域的实际对话中汇编而成。

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Few-shot learning (FSL) aims to recognize new objects with extremely limited training data for each category. Previous efforts are made by either leveraging meta-learning paradigm or novel principles in data augmentation to alleviate this extremely data-scarce problem. In contrast, this paper presents a simple statistical approach, dubbed Instance Credibility Inference (ICI) to exploit the distribution support of unlabeled instances for few-shot learning. Specifically, we first train a linear classifier with the labeled few-shot examples and use it to infer the pseudo-labels for the unlabeled data. To measure the credibility of each pseudo-labeled instance, we then propose to solve another linear regression hypothesis by increasing the sparsity of the incidental parameters and rank the pseudo-labeled instances with their sparsity degree. We select the most trustworthy pseudo-labeled instances alongside the labeled examples to re-train the linear classifier. This process is iterated until all the unlabeled samples are included in the expanded training set, i.e. the pseudo-label is converged for unlabeled data pool. Extensive experiments under two few-shot settings show that our simple approach can establish new state-of-the-arts on four widely used few-shot learning benchmark datasets including miniImageNet, tieredImageNet, CIFAR-FS, and CUB. Our code is available at: https://github.com/Yikai-Wang/ICI-FSL

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