  Most state of the art approaches for Named Entity Recognition rely on hand
crafted features and annotated corpora. Recently Neural network based models
have been proposed which do not require handcrafted features but still require
annotated corpora. However, such annotated corpora may not be available for
many languages. In this paper, we propose a neural network based model which
allows sharing the decoder as well as word and character level parameters
between two languages thereby allowing a resource fortunate language to aid a
resource deprived language. Specifically, we focus on the case when limited
annotated corpora is available in one language ($L_1$) and abundant annotated
corpora is available in another language ($L_2$). Sharing the network
architecture and parameters between $L_1$ and $L_2$ leads to improved
performance in $L_1$. Further, our approach does not require any hand crafted
features but instead directly learns meaningful feature representations from
the training data itself. We experiment with 4 language pairs and show that
indeed in a resource constrained setup (lesser annotated corpora), a model
jointly trained with data from another language performs better than a model
trained only on the limited corpora in one language.
