@InProceedings{zennaki-semmar-besacier:2016:COLING,
  author    = {ZENNAKI, Othman  and  Semmar, Nasredine  and  Besacier, Laurent},
  title     = {Inducing Multilingual Text Analysis Tools Using Bidirectional Recurrent Neural Networks},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {450--460},
  abstract  = {This work focuses on the development of linguistic analysis tools for
	resource-poor languages. We use a parallel corpus to produce a multilingual
	word representation based only on sentence level alignment. This representation
	is combined with the annotated source side (resource-rich language) of the
	parallel corpus to train text analysis tools for resource-poor languages. Our
	approach is based on Recurrent Neural Networks (RNN) and has the following
	advantages: (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.  
	In a previous study, we proposed a method based on Simple RNN to automatically
	induce a Part-Of-Speech (POS) tagger. In this paper, we propose an improvement
	of our neural model. We investigate the Bidirectional RNN and the inclusion of
	external information (for instance low level information from Part-Of-Speech
	tags) in the RNN to train a more complex tagger (for instance, a multilingual
	super sense tagger). We demonstrate the validity and genericity of our method
	by using parallel corpora (obtained by manual or automatic translation). Our
	experiments are conducted to induce cross-lingual POS and super sense taggers.},
  url       = {http://aclweb.org/anthology/C16-1044}
}