This is an interesting paper, investigating (with a team that includes the original authors of the Lottery Ticket paper) whether the initializations that result from BERT pretraining have Lottery Ticket-esque properties with respect to their role as initializations for downstream transfer tasks. As background context, the Lottery Ticket Hypothesis came out of an observation that trained networks could be pruned to remove low-magnitude weights (according to a particular iterative pruning strategy that is a bit more complex than just "prune everything at the end of training"), down to high levels of sparsity (5-40% of original weights, and that those pruned networks not only perform well at the end of training, but also can be "rewound" back to their initialization values (or, in some cases, values from early in training) and retrained in isolation, with the weights you pruned out of the trained network still set to 0, to a comparable level of accuracy. This is thought of as a "winning ticket" because the hypothesis Frankle and Carbin generated is that the reason we benefit from massively overparametrized neural networks is that we are essentially sampling a large number of small subnetworks within the larger ones, and that the more samples we get, the likelier it is we find a "winning ticket" that starts our optimization in a place conducive to further training. In this particular work, the authors investigate a slightly odd variant of the LTH. Instead of looking at training runs that start from random initializations, they look at transfer tasks that start their learning from a massively-pretrained BERT language model. They try to find out: 1) Whether you can find "winning tickets" as subsets of the BERT initialization for a given downstream task 2) Whether those winning tickets generalize, i.e. whether a ticket/pruning mask for one downstream task can also have high performance on another. If that were the case, it would indicate that much of the value of a BERT initialization for transfer tasks could be captured by transferring only a small percentage of BERT's (many) weights, which would be beneficial for compression and mobile applications An interesting wrinkle in the LTH literature is the question of whether true "winning tickets" can be found (in the sense of the network being able to retrain purely from the masked random initializations), or whether it can only retrain to a comparable accuracy by rewinding to an early stage in training, but not the absolute beginning of training. Historically, the former has been difficult and sometimes not possible to find in more complex tasks and networks. https://i.imgur.com/pAF08H3.png One finding of this paper is that, when your starting point is BERT initialization, you can indeed find "winning tickets" in the first sense of being able to rewind the full way back to the beginning of (downstream task) training, and retrain from there. (You can see this above with the results for IMP, Iterative Magnitude Pruning, rolling back to theta-0). This is a bit of an odd finding to parse, since it's not like BERT really is a random initialization itself, but it does suggest that part of the value of BERT is that it contains subnetworks that, from the start of training, are in notional optimization basins that facilitate future training. A negative result in this paper is that, by and large, winning tickets on downstream tasks don't transfer from one to another, and, to the extent that they do transfer, it mostly seems to be according to which tasks had more training samples used in the downstream mask-finding process, rather than any qualitative properties of the task. The one exception to this was if you did further training of the original BERT objective, Masked Language Modeling, as a "downstream task", and took the winning ticket mask from that training, which then transferred to other tasks. This is some validation of the premise that MLM is an unusually good training task in terms of its transfer properties. An important thing to note here is that, even though this hypothesis is intriguing, it's currently quite computationally expensive to find "winning tickets", requiring an iterative pruning and retraining process that takes far longer than an original training run would have. The real goal here, which this is another small step in the hopeful direction of, is being able to analytically specify subnetworks with valuable optimization properties, without having to learn them from data each time (which somewhat defeats the point, if they're only applicable for the task they're trained on, though is potentially useful is they do transfer to some other tasks, as has been shown within a set of image-prediction tasks).