Research on Data Augmentation Strategies and Grammar Error Analysis Techniques in AI Artificial Intelligence Translation
The information processing logic of artificial intelligence can learn and understand language systems,and then provide optimal results in translation work to meet practical application needs.Research combines data augmentation strategies and corpora to train grammar error generation,correction,and detection models.Research and analyze rule-based data augmentation strategies to improve the quality of training data.Using a learner corpus to analyze the results of GEC models of different scales,it was found that the accuracy of the GEC model trained with synthetic data of around 200M is 45％,with the highest recall rate of 24％,and F_The maximum value of 0.5 is 38％.Further training was conducted on the optimized GEC model,resulting in values of 37％,24％,and 34％,respectively.Finally,the results of the grammar error model based on data augmentation strategy under reordering strategy are 75％,43％,and 65％.Therefore,it is proven that the grammar error model based on data augmentation strategy has high detection accuracy and improves technical support for artificial intelligence translation technology.
data augmentation strategylearner corpusgrammar error correctionGEG modelreordering strategy
万方数据
维普
