With shared contextual encoder representation consisting of character and word embedding based models, the proposed architecture provides an effective solution for knowledge transfer across tasks, thus consolidating the ability to perform well on unseen text.
this proposed ar- chitecture is not scalable, the number of decoders scales linearly with the number of attributes.