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.

翻译：文本纠错，尤其是面向更广泛应用场景的语义级纠错，迫切需要提升文本的流畅性与写作效率。本文提出一种基于对抗多任务学习的方法，以增强对中文句子上下文中字词多义性的建模与检测能力。其中，掩码语言模型与评分语言模型被引入为一对兼具耦合性与对抗性的学习任务。此外，引入蒙特卡洛树搜索策略与策略网络，实现具有语义检测的高效中文文本纠错任务。实验在三个数据集上展开，并与五种可比方法进行对比，结果表明本文方法在中文文本纠错任务中能获得良好性能，具有更优的语义合理性。