  In this paper, a new deep reinforcement learning based augmented general
sequence tagging system is proposed. The new system contains two parts: a deep
neural network (DNN) based sequence tagging model and a deep reinforcement
learning (DRL) based augmented tagger. The augmented tagger helps improve
system performance by modeling the data with minority tags. The new system is
evaluated on SLU and NLU sequence tagging tasks using ATIS and CoNLL-2003
benchmark datasets, to demonstrate the new system's outstanding performance on
general tagging tasks. Evaluated by F1 scores, it shows that the new system
outperforms the current state-of-the-art model on ATIS dataset by 1.9% and that
on CoNLL-2003 dataset by 1.4%.
