  Customer feedback can be an important signal for improving commercial machine
translation systems. One solution for fixing specific translation errors is to
remove the related erroneous training instances followed by re-training of the
machine translation system, which we refer to as instance-specific data
filtering. Influence functions (IF) have been shown to be effective in finding
such relevant training examples for classification tasks such as image
classification, toxic speech detection and entailment task. Given a probing
instance, IF find influential training examples by measuring the similarity of
the probing instance with a set of training examples in gradient space. In this
work, we examine the use of influence functions for Neural Machine Translation
(NMT). We propose two effective extensions to a state of the art influence
function and demonstrate on the sub-problem of copied training examples that IF
can be applied more generally than handcrafted regular expressions.
