Information Dropping Data Augmentation for Machine Translation Quality Estimation

Machine translation quality estimation (QE) refers to the quality assessment of machine translations without a given reference translation. Supervised QE models based on neural networks have achieved state-of-the-art results. But this method requires large-scale training data, which requires bilingual experts to create high-quality labels. This is often very costly. Therefore, we propose a sentence-level machine translation QE data augmentation method based on information dropping. Firstly, we calculate the subwords information of the target translation based on the conditional language model. Subsequently, some subwords in the target translation are randomly deleted or replaced. We obtain the pseudo quality score by calculating the remaining information. Finally, the original and augmented data are combined to train the final model. This pseudo-data generation method based on information dropping strategy enables us to obtain more faithful and diverse training samples without requiring additional corpus resources. Experimental results show that we improve the correlation with human judgment by an average of 5.96% in the seven translation directions of the MLQE-PE dataset, while improving the model's robustness to low adequacy samples. In addition, the method does not require any modifications to the model architecture.