  While the role of humans is increasingly recognized in machine learning
community, representation of and interaction with models in current
human-in-the-loop machine learning (HITL-ML) approaches are too low-level and
far-removed from human's conceptual models. We demonstrate HEIDL, a prototype
HITL-ML system that exposes the machine-learned model through high-level,
explainable linguistic expressions formed of predicates representing semantic
structure of text. In HEIDL, human's role is elevated from simply evaluating
model predictions to interpreting and even updating the model logic directly by
enabling interaction with rule predicates themselves. Raising the currency of
interaction to such semantic levels calls for new interaction paradigms between
humans and machines that result in improved productivity for text analytics
model development process. Moreover, by involving humans in the process, the
human-machine co-created models generalize better to unseen data as domain
experts are able to instill their expertise by extrapolating from what has been
learned by automated algorithms from few labelled data.
