Enabling Learning as a Joint Task via Paraphrasing

Human in the loop learning (HiLL) approaches involve a repeated exchange of information between human teachers and learning agents. Rather than adhering to the traditional paradigm of learning from human feedback, wherein an active learning system may repeatedly query a human teacher for information, we consider a shift to a more collaborative learning approach where the algorithmic learner can also share information with a human teacher. We propose that the same interactions types (Showing, Categorizing, Sorting, and Evaluating) that are effective in learning from human feedback should be effective in conveying information from the algorithmic learner, and that doing so will improve learning outcomes. We present examples of how these interactions can be used to share information from the algorithmic learner in the form of \textit{paraphrases}, outline a user study and experimental design for studying the impact of these paraphrases, and present metrics for evaluating their effects on learning and teaching outcomes.