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.

本文介绍了一种名为 HEIDL 的新型人机交互机器学习系统，可以通过高水平的、可解释的语言表达式与人交互，从而提高人类在模型预测、理解以及更新方面的参与度，并且可以使得机器学习模型能够更好地应用于实际场景中。