  Many information retrieval algorithms rely on the notion of a good distance
that allows to efficiently compare objects of different nature. Recently, a new
promising metric called Word Mover's Distance was proposed to measure the
divergence between text passages. In this paper, we demonstrate that this
metric can be extended to incorporate term-weighting schemes and provide more
accurate and computationally efficient matching between documents using
entropic regularization. We evaluate the benefits of both extensions in the
task of cross-lingual document retrieval (CLDR). Our experimental results on
eight CLDR problems suggest that the proposed methods achieve remarkable
improvements in terms of Mean Reciprocal Rank compared to several baselines.
