  While the problem of hallucinations in neural machine translation has long
been recognized, so far the progress on its alleviation is very little. Indeed,
recently it turned out that without artificially encouraging models to
hallucinate, previously existing methods fall short and even the standard
sequence log-probability is more informative. It means that characteristics
internal to the model can give much more information than we expect, and before
using external models and measures, we first need to ask: how far can we go if
we use nothing but the translation model itself ? We propose to use a method
that evaluates the percentage of the source contribution to a generated
translation. Intuitively, hallucinations are translations "detached" from the
source, hence they can be identified by low source contribution. This method
improves detection accuracy for the most severe hallucinations by a factor of 2
and is able to alleviate hallucinations at test time on par with the previous
best approach that relies on external models. Next, if we move away from
internal model characteristics and allow external tools, we show that using
sentence similarity from cross-lingual embeddings further improves these
results.
