Teachers intentionally pick the most informative examples to show their students. However, if the teacher and student are neural networks, the examples that the teacher network learns to give, although effective at teaching the student, are typically uninterpretable. We show that training the student and teacher iteratively, rather than jointly, can produce interpretable teaching strategies. We evaluate interpretability by (1) measuring the similarity of the teacher's emergent strategies to intuitive strategies in each domain and (2) conducting human experiments to evaluate how effective the teacher's strategies are at teaching humans. We show that the teacher network learns to select or generate interpretable, pedagogical examples to teach rule-based, probabilistic, boolean, and hierarchical concepts. Human teachers give informative examples to help their students learn concepts faster and more accurately BID23 BID21 BID4 . For example, suppose a teacher is trying to teach different types of animals to a student. To teach what a "dog" is they would not show the student only images of dalmatians. Instead, they would show different types of dogs, so the student generalizes the word "dog" to all types of dogs, rather than merely dalmatians.Teaching through examples can be seen as a form of communication between a teacher and a student. Recent work on learning emergent communication protocols in deep-learning based agents has been successful at solving a variety of tasks BID7 BID24 BID18 BID5 BID16 . Unfortunately, the protocols learned by the agents are usually uninterpretable to humans , and thus at the moment have limited potential for communication with humans.We hypothesize that one reason the emergent protocols are uninterpretable is because the agents are typically optimized jointly. Consider how this would play out with a teacher network T that selects or generates examples to give to a student network S. If T and S are optimized jointly, then T and S essentially become an encoder and decoder that can learn any arbitrary encoding. T could encode "dog" through a picture of a giraffe and encode "siamese cat" through a picture of a hippo.The examples chosen by T, although effective at teaching S, are unintuitive since S does not learn in the way we expect. On the other hand, picking diverse dog images to communicate the concept of "dog" is an intuitive strategy because it is the effective way to teach given how we implicitly assume a student would interpret the examples. Thus, we believe that S having an interpretable learning strategy is key to the emergence of an interpretable teaching strategy.This raises the question of whether there is an alternative to jointly optimizing T and S, in which S maintains an interpretable learning strategy, and leads T to learn an interpretable teaching strategy. We would ideally like such an alternative to be domain-agnostic. Drawing on inspiration from the cognitive science work on rational pedagogy (see Section 2.1), we propose a simple change:1. Train S on random examples 2. Train T to pick examples for this fixed S
We show that training a student and teacher network iteratively, rather than jointly, can produce emergent, interpretable teaching strategies.
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