  We present new supertaggers trained on HPSG-based treebanks. These treebanks
feature high-quality annotation based on a well-developed linguistic theory and
include diverse and challenging test datasets, beyond the usual WSJ section 23
and Wikipedia data. HPSG supertagging has previously relied on MaxEnt-based
models. We use SVM and neural CRF- and BERT-based methods and show that both
SVM and neural supertaggers achieve considerably higher accuracy compared to
the baseline. Our fine-tuned BERT-based tagger achieves 97.26% accuracy on 1000
sentences from WSJ23 and 93.88% on the completely out-of-domain The Cathedral
and the Bazaar (cb)). We conclude that it therefore makes sense to integrate
these new supertaggers into modern HPSG parsers, and we also hope that the
diverse and difficult datasets we used here will gain more popularity in the
field. We contribute the complete dataset reformatted for token classification.
