In this paper we investigate the use of techniques that can exploit the training instances from different domains to learn a QE model for a specific target domain for which there is a small amount of labeled data.
we are interested in approaches that allow not only learning from one single source domain but also from multiple source domains simultaneously, by leveraging the labels from all available data to improve results in a target domain.