  Keyphrase boundary classification (KBC) is the task of detecting keyphrases
in scientific articles and labelling them with respect to predefined types.
Although important in practice, this task is so far underexplored, partly due
to the lack of labelled data. To overcome this, we explore several auxiliary
tasks, including semantic super-sense tagging and identification of multi-word
expressions, and cast the task as a multi-task learning problem with deep
recurrent neural networks. Our multi-task models perform significantly better
than previous state of the art approaches on two scientific KBC datasets,
particularly for long keyphrases.
