Few-shot slot tagging is an emerging research topic in the field of Natural Language Understanding (NLU). With sufficient annotated data from source domains, the key challenge is how to train and adapt the model to another target domain which only has few labels. Conventional few-shot approaches use all the data from the source domains without considering inter-domain relations and implicitly assume each sample in the domain contributes equally. However, our experiments show that the data distribution bias among different domains will significantly affect the adaption performance. Moreover, transferring knowledge from dissimilar domains will even introduce some extra noises so that affect the performance of models. To tackle this problem, we propose an effective similarity-based method to select data from the source domains. In addition, we propose a Shared-Private Network (SP-Net) for the few-shot slot tagging task. The words from the same class would have some shared features. We extract those shared features from the limited annotated data on the target domain and merge them together as the label embedding to help us predict other unlabelled data on the target domain. The experiment shows that our method outperforms the state-of-the-art approaches with fewer source data. The result also proves that some training data from dissimilar sources are redundant and even negative for the adaption.
翻译:少见的插座标记是自然语言理解(NLU)领域一个新兴的研究课题。 有了来自源域的充足附加说明的数据,关键的挑战是如何培训和将模型调整到另一个只有很少标签的目标域。 常规的少发方法使用源域的所有数据,而没有考虑内部关系,并隐含地假定域内每个样本都有同样的作用。 但是, 我们的实验显示, 不同域间的数据分布偏差将极大地影响适应性。 此外, 从不同域间传输知识甚至会引入一些额外的噪音, 从而影响模型的性能。 为了解决这个问题, 我们提出了一种有效的基于相似性的方法来从源域中选择数据。 此外, 我们建议对少数发源域的标记任务采用共享- 私人网络( SP- Net ) 。 同一类的词会有一些共同的特性。 我们从目标域上有限的附加说明的数据中提取这些共同特征, 并把它们合并在一起作为标签, 帮助我们预测目标域内的其他未加标签的数据。 实验显示, 我们的方法超越了从源域内选择的状态和变异性数据的方法。