Description and demonstration guided data augmentation for sequence tagging

Fine-grained annotations are indispensable for sequence tagging tasks like named entity recognition and aspect-based sentiment analysis, which may incur extremely high time and labor costs. Recent efforts are towards data augmentation which aims to generate synthetic labeled instances. However, most existing methods adopt the random replacement or perturbation strategy under pre-defined constraints, and thus often lead to unstable performance. More importantly, these methods focus on producing more artificial samples yet neglect to make good use of real training samples. In this paper, we propose a novel description and demonstration guided data augmentation (D³A) approach for sequence tagging. On one hand, we collect dependency paths as descriptions to supervise the instance-level augmentation process, such that we can consistently generate high-quality synthetic data. On the other hand, we retrieve semantic or syntactic related features as demonstrations to enhance the learning capability of neural networks under limited training data. We conduct extensive experiments on four sequence tagging datasets with various sizes of training data. The results demonstrate that our proposed D³A approach can significantly improve the performance of sequence tagging, especially in low-resource scenarios.