论文标题
几乎没有散布的插槽标记，并折叠依赖转移和标签增强任务自发投影网络
Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection Network
论文作者
论文摘要
在本文中，我们仅使用几个标记的支撑句子（又称几个）探索插槽标记。与其他少量分类问题相比，很少有射击插槽标记面临着独特的挑战，因为它要求建模标签之间的依赖关系。但是由于标签集的差异，很难将先前学习的标签依赖项应用于看不见的域。为了解决这个问题，我们将崩溃的依赖转移机制引入条件随机场（CRF），以将抽象标签依赖性模式作为过渡分数传递。在几个射击设置中，可以将CRF的排放评分计算为单词与每个标签的表示的相似性。为了计算这种相似性，我们提出了一个基于最先进的少数照片分类模型-TAPNET，通过利用代表标签中的标签名称语义来提出一个标签增强的任务自适应投影网络（L-TAPNET）。实验结果表明，我们的模型在单次设置中的表现明显优于最强的几次学习基线基线。
In this paper, we explore the slot tagging with only a few labeled support sentences (a.k.a. few-shot). Few-shot slot tagging faces a unique challenge compared to the other few-shot classification problems as it calls for modeling the dependencies between labels. But it is hard to apply previously learned label dependencies to an unseen domain, due to the discrepancy of label sets. To tackle this, we introduce a collapsed dependency transfer mechanism into the conditional random field (CRF) to transfer abstract label dependency patterns as transition scores. In the few-shot setting, the emission score of CRF can be calculated as a word's similarity to the representation of each label. To calculate such similarity, we propose a Label-enhanced Task-Adaptive Projection Network (L-TapNet) based on the state-of-the-art few-shot classification model -- TapNet, by leveraging label name semantics in representing labels. Experimental results show that our model significantly outperforms the strongest few-shot learning baseline by 14.64 F1 scores in the one-shot setting.
