  In this work, we present a novel neural network based architecture for
inducing compositional crosslingual word representations. Unlike previously
proposed methods, our method fulfills the following three criteria; it
constrains the word-level representations to be compositional, it is capable of
leveraging both bilingual and monolingual data, and it is scalable to large
vocabularies and large quantities of data. The key component of our approach is
what we refer to as a monolingual inclusion criterion, that exploits the
observation that phrases are more closely semantically related to their
sub-phrases than to other randomly sampled phrases. We evaluate our method on a
well-established crosslingual document classification task and achieve results
that are either comparable, or greatly improve upon previous state-of-the-art
methods. Concretely, our method reaches a level of 92.7% and 84.4% accuracy for
the English to German and German to English sub-tasks respectively. The former
advances the state of the art by 0.9% points of accuracy, the latter is an
absolute improvement upon the previous state of the art by 7.7% points of
accuracy and an improvement of 33.0% in error reduction.
