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This work focuses on the rapid development of linguistic annotation tools forresource-poor languages. We experiment several cross-lingual annotationprojection methods using Recurrent Neural Networks (RNN) models. Thedistinctive feature of our approach is that our multilingual wordrepresentation requires only a parallel corpus between the source and targetlanguage. More precisely, our method has the following characteristics: (a) itdoes not use word alignment information, (b) it does not assume any knowledgeabout foreign languages, which makes it applicable to a wide range ofresource-poor languages, (c) it provides truly multilingual taggers. Weinvestigate both uni- and bi-directional RNN models and propose a method toinclude external information (for instance low level information from POS) inthe RNN to train higher level taggers (for instance, super sense taggers). Wedemonstrate the validity and genericity of our model by using parallel corpora(obtained by manual or automatic translation). Our experiments are conducted toinduce cross-lingual POS and super sense taggers.

Inducing Multilingual Text Analysis Tools Using Bidirectional Recurrent Neural Networks