While quality estimation (QE) can play an important role in the translation process, its effectiveness relies on the availability and quality of training data. For QE in particular, high-quality labeled data is often lacking due to the high-cost and effort associated with labeling such data. Aside from the data scarcity challenge, QE models should also be generalizable, i.e., they should be able to handle data from different domains, both generic and specific. To alleviate these two main issues -- data scarcity and domain mismatch -- this paper combines domain adaptation and data augmentation within a robust QE system. Our method is to first train a generic QE model and then fine-tune it on a specific domain while retaining generic knowledge. Our results show a significant improvement for all the language pairs investigated, better cross-lingual inference, and a superior performance in zero-shot learning scenarios as compared to state-of-the-art baselines.

翻译：质量评估（QE）在翻译过程中可发挥重要作用，但其有效性依赖于训练数据的可用性和质量。尤其对于QE而言，高质量标注数据往往因标注成本高、工作量大而匮乏。除数据稀缺挑战外，QE模型还应具备泛化能力，即能够处理来自不同领域（包括通用领域和特定领域）的数据。为解决数据稀缺与领域不匹配这两个主要问题，本文在稳健的QE系统中融合了领域自适应与数据增强技术。我们的方法是：首先训练通用QE模型，随后在保留通用知识的基础上对特定领域进行微调。结果表明，与最先进基线方法相比，本方法在所研究的所有语言对中均取得显著提升，跨语言推理能力更优，且在零样本学习场景中展现出卓越性能。