An Adversarial Multi-Task Learning Method for Chinese Text Correction
  with Semantic Detection
Fanyu Wang Zhenping Xie
Text correction, especially the semantic correction of more widely used
scenes, is strongly required to improve, for the fluency and writing efficiency
of the text. An adversarial multi-task learning method is proposed to enhance
the modeling and detection ability of character polysemy in Chinese sentence
context. Wherein, two models, the masked language model and scoring language
model, are introduced as a pair of not only coupled but also adversarial
learning tasks. Moreover, the Monte Carlo tree search strategy and a policy
network are introduced to accomplish the efficient Chinese text correction task
with semantic detection. The experiments are executed on three datasets and
five comparable methods, and the experimental results show that our method can
obtain good performance in Chinese text correction task for better semantic
rationality.