  We propose a novel application of self-attention networks towards grammar
induction. We present an attention-based supertagger for a refined type-logical
grammar, trained on constructing types inductively. In addition to achieving a
high overall type accuracy, our model is able to learn the syntax of the
grammar's type system along with its denotational semantics. This lifts the
closed world assumption commonly made by lexicalized grammar supertaggers,
greatly enhancing its generalization potential. This is evidenced both by its
adequate accuracy over sparse word types and its ability to correctly construct
complex types never seen during training, which, to the best of our knowledge,
was as of yet unaccomplished.
