  Domain adaptation considers the problem of generalising a model learnt using
data from a particular source domain to a different target domain. Often it is
difficult to find a suitable single source to adapt from, and one must consider
multiple sources. Using an unrelated source can result in sub-optimal
performance, known as the \emph{negative transfer}. However, it is challenging
to select the appropriate source(s) for classifying a given target instance in
multi-source unsupervised domain adaptation (UDA). We model source-selection as
an attention-learning problem, where we learn attention over sources for a
given target instance. For this purpose, we first independently learn
source-specific classification models, and a relatedness map between sources
and target domains using pseudo-labelled target domain instances. Next, we
learn attention-weights over the sources for aggregating the predictions of the
source-specific models. Experimental results on cross-domain sentiment
classification benchmarks show that the proposed method outperforms prior
proposals in multi-source UDA.
