  In low-resource settings, model transfer can help to overcome a lack of
labeled data for many tasks and domains. However, predicting useful transfer
sources is a challenging problem, as even the most similar sources might lead
to unexpected negative transfer results. Thus, ranking methods based on task
and text similarity may not be sufficient to identify promising sources. To
tackle this problem, we propose a method to automatically determine which and
how many sources should be exploited. For this, we study the effects of model
transfer on sequence labeling across various domains and tasks and show that
our methods based on model similarity and support vector machines are able to
predict promising sources, resulting in performance increases of up to 24 F1
points.
