  Deep learning techniques have recently shown to be successful in many natural
language processing tasks forming state-of-the-art systems. They require,
however, a large amount of annotated data which is often missing. This paper
explores the use of domain-adversarial learning as a regularizer to avoid
overfitting when training domain invariant features for deep, complex neural
networks in low-resource and zero-resource settings in new target domains or
languages. In case of new languages, we show that monolingual word vectors can
be directly used for training without prealignment. Their projection into a
common space can be learnt ad-hoc at training time reaching the final
performance of pretrained multilingual word vectors.
