Quality estimation (QE) is the task of automatically evaluating the quality of translations without human-translated references. Calculating BLEU between the input sentence and round-trip translation (RTT) was once considered as a metric for QE, however, it was found to be a poor predictor of translation quality. Recently, various pre-trained language models have made breakthroughs in NLP tasks by providing semantically meaningful word and sentence embeddings. In this paper, we employ semantic embeddings to RTT-based QE. Our method achieves the highest correlations with human judgments, compared to previous WMT 2019 quality estimation metric task submissions. While backward translation models can be a drawback when using RTT, we observe that with semantic-level metrics, RTT-based QE is robust to the choice of the backward translation system. Additionally, the proposed method shows consistent performance for both SMT and NMT forward translation systems, implying the method does not penalize a certain type of model.

本文介绍了一种利用语义嵌入进行往返翻译的质量估计技术，相较于以往的WMT 2019质量估计任务提交的方法，我们的方法与人类判断之间的相关性最高，且在往返翻译选择上表现鲁棒性较佳。此外，该方法能够针对SMT和NMT等多种类型的前向翻译模型进行一致的性能表现。

重温循环翻译以进行质量评估