Span identification aims at identifying specific text spans from text input and classifying them into pre-defined categories. Different from previous works that merely leverage the Subordinate (SUB) relation (i.e. if a span is an instance of a certain category) to train models, this paper for the first time explores the Peer (PR) relation, which indicates that two spans are instances of the same category and share similar features. Specifically, a novel Peer Data Augmentation (PeerDA) approach is proposed which employs span pairs with the PR relation as the augmentation data for training. PeerDA has two unique advantages: (1) There are a large number of PR span pairs for augmenting the training data. (2) The augmented data can prevent the trained model from over-fitting the superficial span-category mapping by pushing the model to leverage the span semantics. Experimental results on ten datasets over four diverse tasks across seven domains demonstrate the effectiveness of PeerDA. Notably, PeerDA achieves state-of-the-art results on six of them.

翻译：跨度识别旨在从文本输入中识别特定文本跨度并将其分类为预定义类别。与以往仅利用从属关系（即某跨度是否为某类别的实例）训练模型的工作不同，本文首次探索了同伴关系，即两个跨度属于同一类别且具有相似特征的关系。具体而言，提出了一种新颖的同伴数据增强方法，该方法采用具有同伴关系的跨度对作为训练增强数据。PeerDA具有两个独特优势：（1）存在大量同伴关系跨度对可用于增强训练数据；（2）增强数据能通过促使模型利用跨度语义，防止训练模型过拟合浅层的跨度-类别映射。在七个领域四个不同任务的十组数据集上的实验结果表明了PeerDA的有效性。值得注意的是，在其中的六组数据集上，PeerDA取得了最先进的结果。