Machine Translation Quality Estimation (QE) is a task of predicting the
quality of machine translations without relying on any reference. Recently, the
predictor-estimator framework trains the predictor as a feature extractor,
which leverages the extra parallel corpora without QE labels, achieving
promising QE performance. However, we argue that there are gaps between the
predictor and the estimator in both data quality and training objectives, which
preclude QE models from benefiting from a large number of parallel corpora more
directly. We propose a novel framework called DirectQE that provides a direct
pretraining for QE tasks. In DirectQE, a generator is trained to produce pseudo
data that is closer to the real QE data, and a detector is pretrained on these
data with novel objectives that are akin to the QE task. Experiments on widely
used benchmarks show that DirectQE outperforms existing methods, without using
any pretraining models such as BERT. We also give extensive analyses showing
how fixing the two gaps contributes to our improvements.

本文提出 DirectQE 框架，通过直接预训练来提高机器翻译质量估计（QE）任务的性能，其中生成器训练产生更接近真实 QE 数据的伪数据，检测器使用类似 QE 任务的新目标在这些数据上进行预先训练。在广泛使用的基准测试中，DirectQE 胜过现有方法，而且没有使用任何预训练模型，如 BERT。