论文标题
重新访问往返翻译以进行质量估算
Revisiting Round-Trip Translation for Quality Estimation
论文作者
论文摘要
质量估计（QE）是自动评估不经过人类翻译参考的翻译质量的任务。在输入句子和往返翻译（RTT）之间计算BLEU曾经被视为量化宽松的度量，但是，它被认为是翻译质量的差预测指标。最近，通过提供语义上有意义的单词和句子嵌入，各种预训练的语言模型在NLP任务中取得了突破。在本文中，我们将语义嵌入基于RTT的量化宽松。与以前的WMT 2019质量估计指标提交相比，我们的方法与人类判断的相关性最高。虽然使用RTT时向后翻译模型可能是一个缺点，但我们观察到使用语义级指标，基于RTT的量化质量标准对向后翻译系统的选择是可靠的。此外，提出的方法显示了SMT和NMT向前翻译系统的一致性，这意味着该方法不会惩罚某种类型的模型。
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.
