Uncertainty-Aware Label Refinement for Sequence Labeling
Tao Gui, Jiacheng Ye, Qi Zhang, Zhengyan Li, Zichu Fei, Yeyun Gong, Xuanjing Huang · 2020
Conditional random fields (CRF) for label decoding has become ubiquitous in sequence labeling tasks.However, the local label dependencies and inefficient Viterbi decoding have always been a problem to be solved.In this work, we introduce a novel two-stage label decoding framework to model long-term label dependencies, while being much more computationally efficient.A base model first predicts draft labels, and then a novel twostream self-attention model makes refinements on these draft predictions based on longrange label dependencies, which can achieve parallel decoding for a faster prediction.In addition, in order to mitigate the side effects of incorrect draft labels, Bayesian neural networks are used to indicate the labels with a high probability of being wrong, which can greatly assist in preventing error propagation.The experimental results on three sequence labeling benchmarks demonstrated that the proposed method not only outperformed the CRF-based methods but also greatly accelerated the inference process.* Both authors contributed equally.