  Recent work in NLP has shown promising results in training models on large
amounts of tasks to achieve better generalization. However, it is not
well-understood how tasks are related, and how helpful training tasks can be
chosen for a new task. In this work, we investigate whether knowing task
relationships via pairwise task transfer improves choosing one or more source
tasks that help to learn a new target task. We provide TaskWeb, a large-scale
benchmark of pairwise task transfers for 22 NLP tasks using three different
model types, sizes, and adaptation methods, spanning about 25,000 experiments.
Then, we design a new method TaskShop based on our analysis of TaskWeb.
TaskShop uses TaskWeb to estimate the benefit of using a source task for
learning a new target task, and to choose a subset of helpful training tasks
for multi-task training. Our method improves overall rankings and top-k
precision of source tasks by 10% and 38%, respectively. We also use TaskShop to
build much smaller multi-task training sets that improve zero-shot performances
across 11 different target tasks by at least 4.3%.
