  Modal verbs (e.g., "can", "should", or "must") occur highly frequently in
scientific articles. Decoding their function is not straightforward: they are
often used for hedging, but they may also denote abilities and restrictions.
Understanding their meaning is important for various NLP tasks such as writing
assistance or accurate information extraction from scientific text.
  To foster research on the usage of modals in this genre, we introduce the
MIST (Modals In Scientific Text) dataset, which contains 3737 modal instances
in five scientific domains annotated for their semantic, pragmatic, or
rhetorical function. We systematically evaluate a set of competitive neural
architectures on MIST. Transfer experiments reveal that leveraging
non-scientific data is of limited benefit for modeling the distinctions in
MIST. Our corpus analysis provides evidence that scientific communities differ
in their usage of modal verbs, yet, classifiers trained on scientific data
generalize to some extent to unseen scientific domains.
