  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.
