  This work focuses on the rapid development of linguistic annotation tools for
resource-poor languages. We experiment several cross-lingual annotation
projection methods using Recurrent Neural Networks (RNN) models. The
distinctive feature of our approach is that our multilingual word
representation requires only a parallel corpus between the source and target
language. More precisely, our method has the following characteristics: (a) it
does not use word alignment information, (b) it does not assume any knowledge
about foreign languages, which makes it applicable to a wide range of
resource-poor languages, (c) it provides truly multilingual taggers. We
investigate both uni- and bi-directional RNN models and propose a method to
include external information (for instance low level information from POS) in
the RNN to train higher level taggers (for instance, super sense taggers). We
demonstrate the validity and genericity of our model by using parallel corpora
(obtained by manual or automatic translation). Our experiments are conducted to
induce cross-lingual POS and super sense taggers.
