  Acquiring ground truth labels for unlabelled data can be a costly procedure,
since it often requires manual labour that is error-prone. Consequently, the
available amount of labelled data is increasingly reduced due to the
limitations of manual data labelling. It is possible to increase the amount of
labelled data samples by performing automated labelling or crowd-sourcing the
annotation procedure. However, they often introduce noise or uncertainty in the
labelset, that leads to decreased performance of supervised deep learning
methods. On the other hand, weak supervision methods remain robust during noisy
labelsets or can be effective even with low amounts of labelled data. In this
paper we evaluate the effectiveness of a representation learning method that
uses external categorical evidence called "Evidence Transfer", against low
amount of corresponding evidence termed as incomplete evidence. Evidence
transfer is a robust solution against external unknown categorical evidence
that can introduce noise or uncertainty. In our experimental evaluation,
evidence transfer proves to be effective and robust against different levels of
incompleteness, for two types of incomplete evidence.
