  We propose a model for tagging unstructured texts with an arbitrary number of
terms drawn from a tree-structured vocabulary (i.e., an ontology). We treat
this as a special case of sequence-to-sequence learning in which the decoder
begins at the root node of an ontological tree and recursively elects to expand
child nodes as a function of the input text, the current node, and the latent
decoder state. In our experiments the proposed method outperforms
state-of-the-art approaches on the important task of automatically assigning
MeSH terms to biomedical abstracts.
