Automatic evaluation on low-resource language translation suffers from a deficiency of parallel corpora. Round-trip translation could be served as a clever and straightforward technique to alleviate the requirement of the parallel evaluation corpus. However, there was an observation of obscure correlations between the evaluation scores by forward and round-trip translations in the era of statistical machine translation (SMT). In this paper, we report the surprising finding that round-trip translation can be used for automatic evaluation without the references. Firstly, our revisit on the round-trip translation in SMT evaluation unveils that its long-standing misunderstanding is essentially caused by copying mechanism. After removing copying mechanism in SMT, round-trip translation scores can appropriately reflect the forward translation performance. Then, we demonstrate the rectification is overdue as round-trip translation could benefit multiple machine translation evaluation tasks. To be more specific, round-trip translation could be used i) to predict corresponding forward translation scores; ii) to improve the performance of the recently advanced quality estimation model; and iii) to identify adversarial competitors in shared tasks via cross-system verification.

翻译：低资源语言翻译的自动评估面临平行语料库匮乏的问题。回译作为一种巧妙且直接的技术，能够缓解对平行评估语料的需求。然而，在统计机器翻译（SMT）时代，人们观察到前向翻译与回译的评估分数之间存在模糊的相关性。本文报告了一个惊人发现：回译可在无需参考译文的情况下用于自动评估。首先，我们重新审视SMT评估中的回译，揭示其长期存在的误解本质上是由复制机制引起的。在去除SMT中的复制机制后，回译分数能够恰当反映前向翻译的性能。其次，我们表明这一矫正已刻不容缓，因为回译可惠及多项机器翻译评估任务。具体而言，回译可用于：i) 预测对应的前向翻译分数；ii) 提升近期先进质量评估模型的性能；以及iii) 通过跨系统验证识别共享任务中的对抗性竞争者。