We propose a neuralized undirected graphical model called Neural-Hidden-CRF to solve the weakly-supervised sequence labeling problem. Under the umbrella of probabilistic undirected graph theory, the proposed Neural-Hidden-CRF embedded with a hidden CRF layer models the variables of word sequence, latent ground truth sequence, and weak label sequence with the global perspective that undirected graphical models particularly enjoy. In Neural-Hidden-CRF, we can capitalize on the powerful language model BERT or other deep models to provide rich contextual semantic knowledge to the latent ground truth sequence, and use the hidden CRF layer to capture the internal label dependencies. Neural-Hidden-CRF is conceptually simple and empirically powerful. It obtains new state-of-the-art results on one crowdsourcing benchmark and three weak-supervision benchmarks, including outperforming the recent advanced model CHMM by 2.80 F1 points and 2.23 F1 points in average generalization and inference performance, respectively.

翻译：我们提出一种名为Neural-Hidden-CRF的神经化无向图模型，用以解决弱监督序列标注问题。在概率无向图理论框架下，所提出的Neural-Hidden-CRF嵌入隐条件随机场层，以无向图模型特有的全局视角对词序列、潜在真实标签序列和弱标签序列的变量进行建模。在Neural-Hidden-CRF中，可借助强大的语言模型BERT或其他深度模型，为潜在真实标签序列提供丰富的上下文语义知识，并通过隐条件随机场层捕获标签内部的依赖关系。Neural-Hidden-CRF概念简洁且经验性能优越。该方法在一个众包基准和三个弱监督基准上取得了新的最优结果，包括在平均泛化性能和推理性能上分别超越最新先进模型CHMM达2.80个F1分数和2.23个F1分数。