  By leveraging large amounts of product data collected across hundreds of live
e-commerce websites, we construct 1000 unique classification tasks that share
similarly-structured input data, comprised of both text and images. These
classification tasks focus on learning the product hierarchy of different
e-commerce websites, causing many of them to be correlated. Adopting a
multi-modal transformer model, we solve these tasks in unison using multi-task
learning (MTL). Extensive experiments are presented over an initial 100-task
dataset to reveal best practices for "large-scale MTL" (i.e., MTL with more
than 100 tasks). From these experiments, a final, unified methodology is
derived, which is composed of both best practices and new proposals such as
DyPa, a simple heuristic for automatically allocating task-specific parameters
to tasks that could benefit from extra capacity. Using our large-scale MTL
methodology, we successfully train a single model across all 1000 tasks in our
dataset while using minimal task specific parameters, thereby showing that it
is possible to extend several orders of magnitude beyond current efforts in
MTL.
