基于伪数据的机器翻译质量估计模型的训练
Training Machine Translation Quality Estimation Model Based on Pseudo Data

摘要 为向基于深度学习的机器翻译质量估计模型提供高效的训练数据, 提出面向目标数据集的伪数据构造方法, 采用基于伪数据预训练与模型精调相结合的两阶段模型训练方法对模型进行训练, 并针对不同伪数据规模设计实验。结果表明, 在构造得到的伪数据下, 利用两阶段训练方法训练得到的机器翻译质量估计模型给出的得分与人工评分的相关性有显著的提升。
Abstract Aimed at providing efficient training data for neural translation quality estimation model, a pseudo data construction method for target dataset is proposed, the model is trained by two stage model training method: pre training based on pseudo data and fine tuning. The experimental design of different pseudo data scale is carried out. The experiment results show that the machine translation quality estimation model trained by the pseudo data has significantly improved in the correlation between the scores given by human and the artificial scores.