篇章约束的译文质量评估模型

提出一种新的篇章约束辅助的译文质量评估模型,不依赖参考译文,为源文篇章中的每条句子的译文进行打分。首先从句子级别的语义表示和词级别的指代特征的角度建模源文和译文上下文之间的差异,然后设计额外的损失函数,使得模型在预测分数的同时,尽可能地约束两者之间的差异。实验结果表明,所提方法能有效提高译文质量评估的性能,在Pearson相关系数上较基线系统最高可提升6.68个百分点。

Document Constrained Translation Quality Estimation Model

This paper proposes a new translation quality estimation model that does not rely on the reference translation to score the translation of each sentence in the source language. The authors model the sentence-level semantic difference and word-level referential difference between the source and translation and design additional loss function to make the model constrain the differences as much as possible when predicting scores. The experimental results show that proposed method can effectively improve the performance of quality estimation model. Compared with the baseline system, the proposed method improves the Pearson correlation coefficient by up to 6.68 percentage points.