An Adversarial Multi-Task Learning Method for Chinese Text Correction with Semantic Detection
This addresses semantic text correction for Chinese language users, with incremental improvements in modeling character polysemy.
The paper tackles Chinese text correction with semantic detection by proposing an adversarial multi-task learning method that combines masked and scoring language models with Monte Carlo tree search, achieving good performance in semantic rationality on three datasets compared to five methods.
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.