论文标题
尽可能少，尽可能多地：检测过度调节的过度和不足的调整
As Little as Possible, as Much as Necessary: Detecting Over- and Undertranslations with Contrastive Conditioning
论文作者
论文摘要
省略和添加内容是神经机器翻译中的典型问题。我们提出了一种通过现成的翻译模型来检测这种现象的方法。考虑到相应的源或目标序列，使用对比度调节，我们将翻译模型下完整序列的可能性与其部分的可能性进行了比较。这允许在翻译中查明多余的单词，即使在没有参考翻译的情况下，也可以在来源中的未翻译单词。我们方法的准确性与需要自定义质量估计模型的监督方法相媲美。
Omission and addition of content is a typical issue in neural machine translation. We propose a method for detecting such phenomena with off-the-shelf translation models. Using contrastive conditioning, we compare the likelihood of a full sequence under a translation model to the likelihood of its parts, given the corresponding source or target sequence. This allows to pinpoint superfluous words in the translation and untranslated words in the source even in the absence of a reference translation. The accuracy of our method is comparable to a supervised method that requires a custom quality estimation model.
