Deep learning methods, where a computational model learns an intricate representation of a large-scale dataset, yielded dramatic performance improvements in speech recognition and computer vision [LeCun et al., 2015]. When we have seen such improvements, a common factor is the availability of large-scale training data [Deng et al., 2009, Bellemare et al., 2013]. For ad hoc ranking in information retrieval, which is a core problem in the field, we did not initially see dramatic improvements in performance from deep learning methods. This led to questions about whether deep learning methods were helping at all [Yang et al., 2019a]. If large training data sets are a factor, one explanation for this could be that the training sets were too small.